Writing to Learn Statistics in an Advanced Placement Statistics Course

ERIC Educational Resources Information Center

Northrup, Christian Glenn

2012-01-01

This study investigated the use of writing in a statistics classroom to learn if writing provided a rich description of problem-solving processes of students as they solved problems. Through analysis of 329 written samples provided by students, it was determined that writing provided a rich description of problem-solving processes and enabled…

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

The relationship between procrastination, learning strategies and statistics anxiety among Iranian college students: a canonical correlation analysis.

PubMed

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction.

Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis

PubMed Central

Tabelow, Karsten; König, Reinhard; Polzehl, Jörg

2016-01-01

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning. PMID:27303809

Deconstructing Statistical Analysis

ERIC Educational Resources Information Center

Snell, Joel

2014-01-01

Using a very complex statistical analysis and research method for the sake of enhancing the prestige of an article or making a new product or service legitimate needs to be monitored and questioned for accuracy. 1) The more complicated the statistical analysis, and research the fewer the number of learned readers can understand it. This adds a…

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

Diagnosis of students' ability in a statistical course based on Rasch probabilistic outcome

NASA Astrophysics Data System (ADS)

Mahmud, Zamalia; Ramli, Wan Syahira Wan; Sapri, Shamsiah; Ahmad, Sanizah

2017-06-01

Measuring students' ability and performance are important in assessing how well students have learned and mastered the statistical courses. Any improvement in learning will depend on the student's approaches to learning, which are relevant to some factors of learning, namely assessment methods carrying out tasks consisting of quizzes, tests, assignment and final examination. This study has attempted an alternative approach to measure students' ability in an undergraduate statistical course based on the Rasch probabilistic model. Firstly, this study aims to explore the learning outcome patterns of students in a statistics course (Applied Probability and Statistics) based on an Entrance-Exit survey. This is followed by investigating students' perceived learning ability based on four Course Learning Outcomes (CLOs) and students' actual learning ability based on their final examination scores. Rasch analysis revealed that students perceived themselves as lacking the ability to understand about 95% of the statistics concepts at the beginning of the class but eventually they had a good understanding at the end of the 14 weeks class. In terms of students' performance in their final examination, their ability in understanding the topics varies at different probability values given the ability of the students and difficulty of the questions. Majority found the probability and counting rules topic to be the most difficult to learn.

The Relationship Between Procrastination, Learning Strategies and Statistics Anxiety Among Iranian College Students: A Canonical Correlation Analysis

PubMed Central

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468

A Statistical Analysis of the Effect of the Navy’s Tuition Assistance Program: Do Distance Learning Classes Make a Difference?

DTIC Science & Technology

2010-03-01

ANALYSIS OF THE EFFECT OF THE NAVY’S TUITION ASSISTANCE PROGRAM : DO DISTANCE LEARNING CLASSES MAKE A DIFFERENCE? by Jeremy P. McLaughlin March...TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE A Statistical Analysis of the Effect of the Navy’s Tuition Assistance Program : Do...200 words) This thesis analyzes the impact of participation in the Navy’s Tuition Assistance (TA) program on the retention of first-term Navy

Systematic review of learning curves for minimally invasive abdominal surgery: a review of the methodology of data collection, depiction of outcomes, and statistical analysis.

PubMed

Harrysson, Iliana J; Cook, Jonathan; Sirimanna, Pramudith; Feldman, Liane S; Darzi, Ara; Aggarwal, Rajesh

2014-07-01

To determine how minimally invasive surgical learning curves are assessed and define an ideal framework for this assessment. Learning curves have implications for training and adoption of new procedures and devices. In 2000, a review of the learning curve literature was done by Ramsay et al and it called for improved reporting and statistical evaluation of learning curves. Since then, a body of literature is emerging on learning curves but the presentation and analysis vary. A systematic search was performed of MEDLINE, EMBASE, ISI Web of Science, ERIC, and the Cochrane Library from 1985 to August 2012. The inclusion criteria are minimally invasive abdominal surgery formally analyzing the learning curve and English language. 592 (11.1%) of the identified studies met the selection criteria. Time is the most commonly used proxy for the learning curve (508, 86%). Intraoperative outcomes were used in 316 (53%) of the articles, postoperative outcomes in 306 (52%), technical skills in 102 (17%), and patient-oriented outcomes in 38 (6%) articles. Over time, there was evidence of an increase in the relative amount of laparoscopic and robotic studies (P < 0.001) without statistical evidence of a change in the complexity of analysis (P = 0.121). Assessment of learning curves is needed to inform surgical training and evaluate new clinical procedures. An ideal analysis would account for the degree of complexity of individual cases and the inherent differences between surgeons. There is no single proxy that best represents the success of surgery, and hence multiple outcomes should be collected.

Statistical learning in reading: variability in irrelevant letters helps children learn phonics skills.

PubMed

Apfelbaum, Keith S; Hazeltine, Eliot; McMurray, Bob

2013-07-01

Early reading abilities are widely considered to derive in part from statistical learning of regularities between letters and sounds. Although there is substantial evidence from laboratory work to support this, how it occurs in the classroom setting has not been extensively explored; there are few investigations of how statistics among letters and sounds influence how children actually learn to read or what principles of statistical learning may improve learning. We examined 2 conflicting principles that may apply to learning grapheme-phoneme-correspondence (GPC) regularities for vowels: (a) variability in irrelevant units may help children derive invariant relationships and (b) similarity between words may force children to use a deeper analysis of lexical structure. We trained 224 first-grade students on a small set of GPC regularities for vowels, embedded in words with either high or low consonant similarity, and tested their generalization to novel tasks and words. Variability offered a consistent benefit over similarity for trained and new words in both trained and new tasks.

Statistical learning of novel graphotactic constraints in children and adults.

PubMed

Samara, Anna; Caravolas, Markéta

2014-05-01

The current study explored statistical learning processes in the acquisition of orthographic knowledge in school-aged children and skilled adults. Learning of novel graphotactic constraints on the position and context of letter distributions was induced by means of a two-phase learning task adapted from Onishi, Chambers, and Fisher (Cognition, 83 (2002) B13-B23). Following incidental exposure to pattern-embedding stimuli in Phase 1, participants' learning generalization was tested in Phase 2 with legality judgments about novel conforming/nonconforming word-like strings. Test phase performance was above chance, suggesting that both types of constraints were reliably learned even after relatively brief exposure. As hypothesized, signal detection theory d' analyses confirmed that learning permissible letter positions (d'=0.97) was easier than permissible neighboring letter contexts (d'=0.19). Adults were more accurate than children in all but a strict analysis of the contextual constraints condition. Consistent with the statistical learning perspective in literacy, our results suggest that statistical learning mechanisms contribute to children's and adults' acquisition of knowledge about graphotactic constraints similar to those existing in their orthography. Copyright © 2013 Elsevier Inc. All rights reserved.

Comparison of student's learning achievement through realistic mathematics education (RME) approach and problem solving approach on grade VII

NASA Astrophysics Data System (ADS)

Ilyas, Muhammad; Salwah

2017-02-01

The type of this research was experiment. The purpose of this study was to determine the difference and the quality of student's learning achievement between students who obtained learning through Realistic Mathematics Education (RME) approach and students who obtained learning through problem solving approach. This study was a quasi-experimental research with non-equivalent experiment group design. The population of this study was all students of grade VII in one of junior high school in Palopo, in the second semester of academic year 2015/2016. Two classes were selected purposively as sample of research that was: year VII-5 as many as 28 students were selected as experiment group I and VII-6 as many as 23 students were selected as experiment group II. Treatment that used in the experiment group I was learning by RME Approach, whereas in the experiment group II by problem solving approach. Technique of data collection in this study gave pretest and posttest to students. The analysis used in this research was an analysis of descriptive statistics and analysis of inferential statistics using t-test. Based on the analysis of descriptive statistics, it can be concluded that the average score of students' mathematics learning after taught using problem solving approach was similar to the average results of students' mathematics learning after taught using realistic mathematics education (RME) approach, which are both at the high category. In addition, It can also be concluded that; (1) there was no difference in the results of students' mathematics learning taught using realistic mathematics education (RME) approach and students who taught using problem solving approach, (2) quality of learning achievement of students who received RME approach and problem solving approach learning was same, which was at the high category.

How Much Math Do Students Need to Succeed in Business and Economics Statistics? An Ordered Probit Analysis

ERIC Educational Resources Information Center

Green, Jeffrey J.; Stone, Courtenay C.; Zegeye, Abera; Charles, Thomas A.

2009-01-01

Because statistical analysis requires the ability to use mathematics, students typically are required to take one or more prerequisite math courses prior to enrolling in the business statistics course. Despite these math prerequisites, however, many students find it difficult to learn business statistics. In this study, we use an ordered probit…

Impact of e-learning on nurses' and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis.

PubMed

Lahti, Mari; Hätönen, Heli; Välimäki, Maritta

2014-01-01

To review the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction related to e-learning. We conducted a systematic review and meta-analysis of randomized controlled trials (RCT) to assess the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction. Electronic databases including MEDLINE (1948-2010), CINAHL (1981-2010), Psychinfo (1967-2010) and Eric (1966-2010) were searched in May 2010 and again in December 2010. All RCT studies evaluating the effectiveness of e-learning and differentiating between traditional learning methods among nurses were included. Data was extracted related to the purpose of the trial, sample, measurements used, index test results and reference standard. An extraction tool developed for Cochrane reviews was used. Methodological quality of eligible trials was assessed. 11 trials were eligible for inclusion in the analysis. We identified 11 randomized controlled trials including a total of 2491 nurses and student nurses'. First, the random effect size for four studies showed some improvement associated with e-learning compared to traditional techniques on knowledge. However, the difference was not statistically significant (p=0.39, MD 0.44, 95% CI -0.57 to 1.46). Second, one study reported a slight impact on e-learning on skills, but the difference was not statistically significant, either (p=0.13, MD 0.03, 95% CI -0.09 to 0.69). And third, no results on nurses or student nurses' satisfaction could be reported as the statistical data from three possible studies were not available. Overall, there was no statistical difference between groups in e-learning and traditional learning relating to nurses' or student nurses' knowledge, skills and satisfaction. E-learning can, however, offer an alternative method of education. In future, more studies following the CONSORT and QUOROM statements are needed to evaluate the effects of these interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time Advice and Learning Questions in Computer Simulations

ERIC Educational Resources Information Center

Rey, Gunter Daniel

2011-01-01

Students (N = 101) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without time advice) x 3 (with learning questions and corrective feedback, with…

Employer Learning and the Signaling Value of Education. National Longitudinal Surveys Discussion Paper.

ERIC Educational Resources Information Center

Altonji, Joseph G.; Pierret, Charles R.

A statistical analysis was performed to test the hypothesis that, if profit-maximizing firms have limited information about the general productivity of new workers, they may choose to use easily observable characteristics such as years of education to discriminate statistically among workers. Information about employer learning was obtained by…

Continuing the Conversation about Face-to-Face, Online, and Blended Learning a Meta-Analysis of Empirical Literature 2006-2017

ERIC Educational Resources Information Center

Wandera, Silas

2017-01-01

This paper serves two purposes. First, it statistically compares learning outcomes of face-to-face, online and blended learning instruction. Then it looks at the instructional practices that are associated with effective blended and online learning. A meta-analysis of 30 studies, with 3,687 participants, resulted in 36 effect sizes. The contrasts…

Development of computer-assisted instruction application for statistical data analysis android platform as learning resource

NASA Astrophysics Data System (ADS)

Hendikawati, P.; Arifudin, R.; Zahid, M. Z.

2018-03-01

This study aims to design an android Statistics Data Analysis application that can be accessed through mobile devices to making it easier for users to access. The Statistics Data Analysis application includes various topics of basic statistical along with a parametric statistics data analysis application. The output of this application system is parametric statistics data analysis that can be used for students, lecturers, and users who need the results of statistical calculations quickly and easily understood. Android application development is created using Java programming language. The server programming language uses PHP with the Code Igniter framework, and the database used MySQL. The system development methodology used is the Waterfall methodology with the stages of analysis, design, coding, testing, and implementation and system maintenance. This statistical data analysis application is expected to support statistical lecturing activities and make students easier to understand the statistical analysis of mobile devices.

How Social Network Position Relates to Knowledge Building in Online Learning Communities

ERIC Educational Resources Information Center

Wang, Lu

2010-01-01

Social Network Analysis, Statistical Analysis, Content Analysis and other research methods were used to research online learning communities at Capital Normal University, Beijing. Analysis of the two online courses resulted in the following conclusions: (1) Social networks of the two online courses form typical core-periphery structures; (2)…

Evidence for social learning in wild lemurs (Lemur catta).

PubMed

Kendal, Rachel L; Custance, Deborah M; Kendal, Jeremy R; Vale, Gillian; Stoinski, Tara S; Rakotomalala, Nirina Lalaina; Rasamimanana, Hantanirina

2010-08-01

Interest in social learning has been fueled by claims of culture in wild animals. These remain controversial because alternative explanations to social learning, such as asocial learning or ecological differences, remain difficult to refute. Compared with laboratory-based research, the study of social learning in natural contexts is in its infancy. Here, for the first time, we apply two new statistical methods, option-bias analysis and network-based diffusion analysis, to data from the wild, complemented by standard inferential statistics. Contrary to common thought regarding the cognitive abilities of prosimian primates, our evidence is consistent with social learning within subgroups in the ring-tailed lemur (Lemur catta), supporting the theory of directed social learning (Coussi-Korbel & Fragaszy, 1995). We also caution that, as the toolbox for capturing social learning in natural contexts grows, care is required in ensuring that the methods employed are appropriate-in particular, regarding social dynamics among study subjects. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.

Comparative analysis of positive and negative attitudes toward statistics

NASA Astrophysics Data System (ADS)

Ghulami, Hassan Rahnaward; Ab Hamid, Mohd Rashid; Zakaria, Roslinazairimah

2015-02-01

Many statistics lecturers and statistics education researchers are interested to know the perception of their students' attitudes toward statistics during the statistics course. In statistics course, positive attitude toward statistics is a vital because it will be encourage students to get interested in the statistics course and in order to master the core content of the subject matters under study. Although, students who have negative attitudes toward statistics they will feel depressed especially in the given group assignment, at risk for failure, are often highly emotional, and could not move forward. Therefore, this study investigates the students' attitude towards learning statistics. Six latent constructs have been the measurement of students' attitudes toward learning statistic such as affect, cognitive competence, value, difficulty, interest, and effort. The questionnaire was adopted and adapted from the reliable and validate instrument of Survey of Attitudes towards Statistics (SATS). This study is conducted among engineering undergraduate engineering students in the university Malaysia Pahang (UMP). The respondents consist of students who were taking the applied statistics course from different faculties. From the analysis, it is found that the questionnaire is acceptable and the relationships among the constructs has been proposed and investigated. In this case, students show full effort to master the statistics course, feel statistics course enjoyable, have confidence that they have intellectual capacity, and they have more positive attitudes then negative attitudes towards statistics learning. In conclusion in terms of affect, cognitive competence, value, interest and effort construct the positive attitude towards statistics was mostly exhibited. While negative attitudes mostly exhibited by difficulty construct.

Learning (Not) to Talk about Race: Investigating What Doctoral Students Learn about Race Variables and Statistical Modeling

ERIC Educational Resources Information Center

Armijo, Michael; Lundy-Wagner, Valerie; Merrill, Elizabeth

2012-01-01

This paper asks how doctoral students understand the use of race variables in statistical modeling. More specifically, it examines how doctoral students at two universities are trained to define, operationalize, and analyze race variables. The authors interviewed students and instructors in addition to conducting a document analysis of their texts…

The effect of project-based learning on students' statistical literacy levels for data representation

NASA Astrophysics Data System (ADS)

Koparan, Timur; Güven, Bülent

2015-07-01

The point of this study is to define the effect of project-based learning approach on 8th Grade secondary-school students' statistical literacy levels for data representation. To achieve this goal, a test which consists of 12 open-ended questions in accordance with the views of experts was developed. Seventy 8th grade secondary-school students, 35 in the experimental group and 35 in the control group, took this test twice, one before the application and one after the application. All the raw scores were turned into linear points by using the Winsteps 3.72 modelling program that makes the Rasch analysis and t-tests, and an ANCOVA analysis was carried out with the linear points. Depending on the findings, it was concluded that the project-based learning approach increases students' level of statistical literacy for data representation. Students' levels of statistical literacy before and after the application were shown through the obtained person-item maps.

Statistical Literacy: Developing a Youth and Adult Education Statistical Project

ERIC Educational Resources Information Center

Conti, Keli Cristina; Lucchesi de Carvalho, Dione

2014-01-01

This article focuses on the notion of literacy--general and statistical--in the analysis of data from a fieldwork research project carried out as part of a master's degree that investigated the teaching and learning of statistics in adult education mathematics classes. We describe the statistical context of the project that involved the…

Applying Statistics in the Undergraduate Chemistry Laboratory: Experiments with Food Dyes.

ERIC Educational Resources Information Center

Thomasson, Kathryn; Lofthus-Merschman, Sheila; Humbert, Michelle; Kulevsky, Norman

1998-01-01

Describes several experiments to teach different aspects of the statistical analysis of data using household substances and a simple analysis technique. Each experiment can be performed in three hours. Students learn about treatment of spurious data, application of a pooled variance, linear least-squares fitting, and simultaneous analysis of dyes…

Learning Model and Form of Assesment toward the Inferensial Statistical Achievement by Controlling Numeric Thinking Skills

ERIC Educational Resources Information Center

Widiana, I. Wayan; Jampel, I. Nyoman

2016-01-01

This study aimed to find out the effect of learning model and form of assessment toward inferential statistical achievement after controlling numeric thinking skills. This study was quasi experimental study with 130 students as the sample. The data analysis used ANCOVA. After controlling numeric thinking skills, the result of this study show that:…

Using Technology to Prompt Good Questions about Distributions in Statistics

ERIC Educational Resources Information Center

Nabbout-Cheiban, Marie; Fisher, Forest; Edwards, Michael Todd

2017-01-01

The Common Core State Standards for Mathematics envisions data analysis as a key component of K-grade 12 mathematics instruction with statistics introduced in the early grades. Nonetheless, deficiencies in statistical learning persist throughout elementary school and beyond. Too often, mathematics teachers lack the statistical knowledge for…

Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

NASA Technical Reports Server (NTRS)

He, Yuning; Davies, Misty Dawn

2014-01-01

The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

An Analysis of Factors Affecting Student Perceptions in a Blended Learning Environment

ERIC Educational Resources Information Center

Peruso, Florence Mary

2012-01-01

The current quantitative study measured the perceptions of students towards online-only learning and towards blended-hybrid learning. Descriptive statistics were implemented to analyze the data from a Likert-type survey, administered to students in degree-seeking programs at an institution of higher learning. A "t"-test and…

Predicting Knowledge Workers' Participation in Voluntary Learning with Employee Characteristics and Online Learning Tools

ERIC Educational Resources Information Center

Hicks, Catherine

2018-01-01

Purpose: This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools. Design/methodology/approach: Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models. Findings: Findings are presented for over 40,000…

Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M

Technological Tools in the Introductory Statistics Classroom: Effects on Student Understanding of Inferential Statistics

ERIC Educational Resources Information Center

Meletiou-Mavrotheris, Maria

2004-01-01

While technology has become an integral part of introductory statistics courses, the programs typically employed are professional packages designed primarily for data analysis rather than for learning. Findings from several studies suggest that use of such software in the introductory statistics classroom may not be very effective in helping…

Methods of learning in statistical education: Design and analysis of a randomized trial

NASA Astrophysics Data System (ADS)

Boyd, Felicity Turner

Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus nonparticipants or controls, adjusting for other performance predictors. Students who preferred learning by reflective observation and active experimentation experienced improved performance through internet learning (5.9 points, 95% CI: 1.2, 10.6) and cooperative learning (2.9 points, 95% CI: 0.6, 5.2), respectively. Learning style did not influence study participation. Conclusions. No performance differences by group were observed by intent-to-treat analysis. Participation in active learning appears to improve student performance in an introductory biostatistics course and provides opportunities for enhancing understanding beyond that attained in traditional didactic classrooms.

Physics-based statistical learning approach to mesoscopic model selection.

PubMed

Taverniers, Søren; Haut, Terry S; Barros, Kipton; Alexander, Francis J; Lookman, Turab

2015-11-01

In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.

Functional brain networks for learning predictive statistics.

PubMed

Giorgio, Joseph; Karlaftis, Vasilis M; Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew; Kourtzi, Zoe

2017-08-18

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Language Learning Strategy Use and Reading Achievement

ERIC Educational Resources Information Center

Ghafournia, Narjes

2014-01-01

The current study investigated the differences across the varying levels of EFL learners in the frequency and choice of learning strategies. Using a reading test, questionnaire, and parametric statistical analysis, the findings yielded up discrepancies among the participants in the implementation of language-learning strategies concerning their…

Twenty Years of MALL Project Implementation: A Meta-Analysis of Learning Outcomes

ERIC Educational Resources Information Center

Burston, Jack

2015-01-01

Despite the hundreds of Mobile-Assisted Language Learning (MALL) publications over the past twenty years, statistically reliable measures of learning outcomes are few and far between. In part, this is due to the fact that well over half of all MALL-related studies report no objectively quantifiable learning outcomes, either because they did not…

A Meta-Analysis of Suggestopedia, Suggestology, Suggestive-accelerative Learning and Teaching (SALT), and Super-learning.

ERIC Educational Resources Information Center

Moon, Charles E.; And Others

Forty studies using one or more components of Lozanov's method of suggestive-accelerative learning and teaching were identified from a search of all issues of the "Journal of Suggestive-Accelerative Learning and Teaching." Fourteen studies contained sufficient statistics to compute effect sizes. The studies were coded according to substantive and…

Learner Characteristics Predict Performance and Confidence in E-Learning: An Analysis of User Behavior and Self-Evaluation

ERIC Educational Resources Information Center

Jeske, Debora; Roßnagell, Christian Stamov; Backhaus, Joy

2014-01-01

We examined the role of learner characteristics as predictors of four aspects of e-learning performance, including knowledge test performance, learning confidence, learning efficiency, and navigational effectiveness. We used both self reports and log file records to compute the relevant statistics. Regression analyses showed that both need for…

Effects of Instructional Design with Mental Model Analysis on Learning.

ERIC Educational Resources Information Center

Hong, Eunsook

This paper presents a model for systematic instructional design that includes mental model analysis together with the procedures used in developing computer-based instructional materials in the area of statistical hypothesis testing. The instructional design model is based on the premise that the objective for learning is to achieve expert-like…

Inquiring the Most Critical Teacher's Technology Education Competences in the Highest Efficient Technology Education Learning Organization

ERIC Educational Resources Information Center

Yung-Kuan, Chan; Hsieh, Ming-Yuan; Lee, Chin-Feng; Huang, Chih-Cheng; Ho, Li-Chih

2017-01-01

Under the hyper-dynamic education situation, this research, in order to comprehensively explore the interplays between Teacher Competence Demands (TCD) and Learning Organization Requests (LOR), cross-employs the data refined method of Descriptive Statistics (DS) method and Analysis of Variance (ANOVA) and Principal Components Analysis (PCA)…

Mutual interference between statistical summary perception and statistical learning.

PubMed

Zhao, Jiaying; Ngo, Nhi; McKendrick, Ryan; Turk-Browne, Nicholas B

2011-09-01

The visual system is an efficient statistician, extracting statistical summaries over sets of objects (statistical summary perception) and statistical regularities among individual objects (statistical learning). Although these two kinds of statistical processing have been studied extensively in isolation, their relationship is not yet understood. We first examined how statistical summary perception influences statistical learning by manipulating the task that participants performed over sets of objects containing statistical regularities (Experiment 1). Participants who performed a summary task showed no statistical learning of the regularities, whereas those who performed control tasks showed robust learning. We then examined how statistical learning influences statistical summary perception by manipulating whether the sets being summarized contained regularities (Experiment 2) and whether such regularities had already been learned (Experiment 3). The accuracy of summary judgments improved when regularities were removed and when learning had occurred in advance. In sum, calculating summary statistics impeded statistical learning, and extracting statistical regularities impeded statistical summary perception. This mutual interference suggests that statistical summary perception and statistical learning are fundamentally related.

Comparison between project-based learning and discovery learning toward students' metacognitive strategies on global warming concept

NASA Astrophysics Data System (ADS)

Tumewu, Widya Anjelia; Wulan, Ana Ratna; Sanjaya, Yayan

2017-05-01

The purpose of this study was to know comparing the effectiveness of learning using Project-based learning (PjBL) and Discovery Learning (DL) toward students metacognitive strategies on global warming concept. A quasi-experimental research design with a The Matching-Only Pretest-Posttest Control Group Design was used in this study. The subjects were students of two classes 7th grade of one of junior high school in Bandung City, West Java of 2015/2016 academic year. The study was conducted on two experimental class, that were project-based learning treatment on the experimental class I and discovery learning treatment was done on the experimental class II. The data was collected through questionnaire to know students metacognitive strategies. The statistical analysis showed that there were statistically significant differences in students metacognitive strategies between project-based learning and discovery learning.

Targeting Change: Assessing a Faculty Learning Community Focused on Increasing Statistics Content in Life Science Curricula

ERIC Educational Resources Information Center

Parker, Loran Carleton; Gleichsner, Alyssa M.; Adedokun, Omolola A.; Forney, James

2016-01-01

Transformation of research in all biological fields necessitates the design, analysis and, interpretation of large data sets. Preparing students with the requisite skills in experimental design, statistical analysis, and interpretation, and mathematical reasoning will require both curricular reform and faculty who are willing and able to integrate…

Teaching Statistics from the Operating Table: Minimally Invasive and Maximally Educational

ERIC Educational Resources Information Center

Nowacki, Amy S.

2015-01-01

Statistics courses that focus on data analysis in isolation, discounting the scientific inquiry process, may not motivate students to learn the subject. By involving students in other steps of the inquiry process, such as generating hypotheses and data, students may become more interested and vested in the analysis step. Additionally, such an…

Which statistics should tropical biologists learn?

PubMed

Loaiza Velásquez, Natalia; González Lutz, María Isabel; Monge-Nájera, Julián

2011-09-01

Tropical biologists study the richest and most endangered biodiversity in the planet, and in these times of climate change and mega-extinctions, the need for efficient, good quality research is more pressing than in the past. However, the statistical component in research published by tropical authors sometimes suffers from poor quality in data collection; mediocre or bad experimental design and a rigid and outdated view of data analysis. To suggest improvements in their statistical education, we listed all the statistical tests and other quantitative analyses used in two leading tropical journals, the Revista de Biología Tropical and Biotropica, during a year. The 12 most frequent tests in the articles were: Analysis of Variance (ANOVA), Chi-Square Test, Student's T Test, Linear Regression, Pearson's Correlation Coefficient, Mann-Whitney U Test, Kruskal-Wallis Test, Shannon's Diversity Index, Tukey's Test, Cluster Analysis, Spearman's Rank Correlation Test and Principal Component Analysis. We conclude that statistical education for tropical biologists must abandon the old syllabus based on the mathematical side of statistics and concentrate on the correct selection of these and other procedures and tests, on their biological interpretation and on the use of reliable and friendly freeware. We think that their time will be better spent understanding and protecting tropical ecosystems than trying to learn the mathematical foundations of statistics: in most cases, a well designed one-semester course should be enough for their basic requirements.

The Statistical Interpretation of Classical Thermodynamic Heating and Expansion Processes

ERIC Educational Resources Information Center

Cartier, Stephen F.

2011-01-01

A statistical model has been developed and applied to interpret thermodynamic processes typically presented from the macroscopic, classical perspective. Through this model, students learn and apply the concepts of statistical mechanics, quantum mechanics, and classical thermodynamics in the analysis of the (i) constant volume heating, (ii)…

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Learning curves for single incision and conventional laparoscopic right hemicolectomy: a multidimensional analysis.

PubMed

Park, Yoonah; Yong, Yuen Geng; Yun, Seong Hyeon; Jung, Kyung Uk; Huh, Jung Wook; Cho, Yong Beom; Kim, Hee Cheol; Lee, Woo Yong; Chun, Ho-Kyung

2015-05-01

This study aimed to compare the learning curves and early postoperative outcomes for conventional laparoscopic (CL) and single incision laparoscopic (SIL) right hemicolectomy (RHC). This retrospective study included the initial 35 cases in each group. Learning curves were evaluated by the moving average of operative time, mean operative time of every five consecutive cases, and cumulative sum (CUSUM) analysis. The learning phase was considered overcome when the moving average of operative times reached a plateau, and when the mean operative time of every five consecutive cases reached a low point and subsequently did not vary by more than 30 minutes. Six patients with missing data in the CL RHC group were excluded from the analyses. According to the mean operative time of every five consecutive cases, learning phase of SIL and CL RHC was completed between 26 and 30 cases, and 16 and 20 cases, respectively. Moving average analysis revealed that approximately 31 (SIL) and 25 (CL) cases were needed to complete the learning phase, respectively. CUSUM analysis demonstrated that 10 (SIL) and two (CL) cases were required to reach a steady state of complication-free performance, respectively. Postoperative complications rate was higher in SIL than in CL group, but the difference was not statistically significant (17.1% vs. 3.4%). The learning phase of SIL RHC is longer than that of CL RHC. Early oncological outcomes of both techniques were comparable. However, SIL RHC had a statistically insignificant higher complication rate than CL RHC during the learning phase.

Practicality of Elementary Statistics Module Based on CTL Completed by Instructions on Using Software R

NASA Astrophysics Data System (ADS)

Delyana, H.; Rismen, S.; Handayani, S.

2018-04-01

This research is a development research using 4-D design model (define, design, develop, and disseminate). The results of the define stage are analyzed for the needs of the following; Syllabus analysis, textbook analysis, student characteristics analysis and literature analysis. The results of textbook analysis obtained the description that of the two textbooks that must be owned by students also still difficulty in understanding it, the form of presentation also has not facilitated students to be independent in learning to find the concept, textbooks are also not equipped with data processing referrals by using software R. The developed module is considered valid by the experts. Further field trials are conducted to determine the practicality and effectiveness. The trial was conducted to the students of Mathematics Education Study Program of STKIP PGRI which was taken randomly which has not taken Basic Statistics Course that is as many as 4 people. Practical aspects of attention are easy, time efficient, easy to interpret, and equivalence. The practical value in each aspect is 3.7; 3.79, 3.7 and 3.78. Based on the results of the test students considered that the module has been very practical use in learning. This means that the module developed can be used by students in Elementary Statistics learning.

Observational Word Learning: Beyond Propose-But-Verify and Associative Bean Counting.

PubMed

Roembke, Tanja; McMurray, Bob

2016-04-01

Learning new words is difficult. In any naming situation, there are multiple possible interpretations of a novel word. Recent approaches suggest that learners may solve this problem by tracking co-occurrence statistics between words and referents across multiple naming situations (e.g. Yu & Smith, 2007), overcoming the ambiguity in any one situation. Yet, there remains debate around the underlying mechanisms. We conducted two experiments in which learners acquired eight word-object mappings using cross-situational statistics while eye-movements were tracked. These addressed four unresolved questions regarding the learning mechanism. First, eye-movements during learning showed evidence that listeners maintain multiple hypotheses for a given word and bring them all to bear in the moment of naming. Second, trial-by-trial analyses of accuracy suggested that listeners accumulate continuous statistics about word/object mappings, over and above prior hypotheses they have about a word. Third, consistent, probabilistic context can impede learning, as false associations between words and highly co-occurring referents are formed. Finally, a number of factors not previously considered in prior analysis impact observational word learning: knowledge of the foils, spatial consistency of the target object, and the number of trials between presentations of the same word. This evidence suggests that observational word learning may derive from a combination of gradual statistical or associative learning mechanisms and more rapid real-time processes such as competition, mutual exclusivity and even inference or hypothesis testing.

A Preliminary Analysis of the Theoretical Parameters of Organizaational Learning.

DTIC Science & Technology

1995-09-01

PARAMETERS OF ORGANIZATIONAL LEARNING THESIS Presented to the Faculty of the Graduate School of Logistics and Acquisition Management of the Air...Organizational Learning Parameters in the Knowledge Acquisition Category 2~™ 2-3. Organizational Learning Parameters in the Information Distribution Category...Learning Refined Scale 4-94 4-145. Composition of Refined Scale 4 Knowledge Flow 4-95 4-146. Cronbach’s Alpha Statistics for the Complete Knowledge Flow

Instructional Advice, Time Advice and Learning Questions in Computer Simulations

ERIC Educational Resources Information Center

Rey, Gunter Daniel

2010-01-01

Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2…

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Toward User Interfaces and Data Visualization Criteria for Learning Design of Digital Textbooks

ERIC Educational Resources Information Center

Railean, Elena

2014-01-01

User interface and data visualisation criteria are central issues in digital textbooks design. However, when applying mathematical modelling of learning process to the analysis of the possible solutions, it could be observed that results differ. Mathematical learning views cognition in on the base on statistics and probability theory, graph…

Methods of Learning in Statistical Education: A Randomized Trial of Public Health Graduate Students

ERIC Educational Resources Information Center

Enders, Felicity Boyd; Diener-West, Marie

2006-01-01

A randomized trial of 265 consenting students was conducted within an introductory biostatistics course: 69 received eight small group cooperative learning sessions; 97 accessed internet learning sessions; 96 received no intervention. Effect on examination score (95% CI) was assessed by intent-to-treat analysis and by incorporating reported…

Non-linear learning in online tutorial to enhance students’ knowledge on normal distribution application topic

NASA Astrophysics Data System (ADS)

Kartono; Suryadi, D.; Herman, T.

2018-01-01

This study aimed to analyze the enhancement of non-linear learning (NLL) in the online tutorial (OT) content to students’ knowledge of normal distribution application (KONDA). KONDA is a competence expected to be achieved after students studied the topic of normal distribution application in the course named Education Statistics. The analysis was performed by quasi-experiment study design. The subject of the study was divided into an experimental class that was given OT content in NLL model and a control class which was given OT content in conventional learning (CL) model. Data used in this study were the results of online objective tests to measure students’ statistical prior knowledge (SPK) and students’ pre- and post-test of KONDA. The statistical analysis test of a gain score of KONDA of students who had low and moderate SPK’s scores showed students’ KONDA who learn OT content with NLL model was better than students’ KONDA who learn OT content with CL model. Meanwhile, for students who had high SPK’s scores, the gain score of students who learn OT content with NLL model had relatively similar with the gain score of students who learn OT content with CL model. Based on those findings it could be concluded that the NLL model applied to OT content could enhance KONDA of students in low and moderate SPK’s levels. Extra and more challenging didactical situation was needed for students in high SPK’s level to achieve the significant gain score.

Statistics Poster Challenge for Schools

ERIC Educational Resources Information Center

Payne, Brad; Freeman, Jenny; Stillman, Eleanor

2013-01-01

The analysis and interpretation of data are important life skills. A poster challenge for schoolchildren provides an innovative outlet for these skills and demonstrates their relevance to daily life. We discuss our Statistics Poster Challenge and the lessons we have learned.

E-Learning in Croatian Higher Education: An Analysis of Students' Perceptions

NASA Astrophysics Data System (ADS)

Dukić, Darko; Andrijanić, Goran

2010-06-01

Over the last years, e-learning has taken an important role in Croatian higher education as a result of strategies defined and measures undertaken. Nonetheless, in comparison to the developed countries, the achievements in e-learning implementation are still unsatisfactory. Therefore, the efforts to advance e-learning within Croatian higher education need to be intensified. It is further necessary to undertake ongoing activities in order to solve possible problems in e-learning system functioning, which requires the development of adequate evaluation instruments and methods. One of the key steps in this process would be examining and analyzing users' attitudes. This paper presents a study of Croatian students' perceptions with regard to certain aspects of e-learning usage. Given the character of this research, adequate statistical methods were required for the data processing. The results of the analysis indicate that, for the most part, Croatian students have positive perceptions of e-learning, particularly as support to time-honored forms of teaching. However, they are not prepared to completely give up the traditional classroom. Using factor analysis, we identified four underlying factors of a collection of variables related to students' perceptions of e-learning. Furthermore, a certain number of statistically significant differences in student attitudes have been confirmed, in terms of gender and year of study. In our study we used discriminant analysis to determine discriminant functions that distinguished defined groups of students. With this research we managed to a certain degree to alleviate the current data insufficiency in the area of e-learning evaluation among Croatian students. Since this type of learning is gaining in importance within higher education, such analyses have to be conducted continuously.

Statistical Analysis Tools for Learning in Engineering Laboratories.

ERIC Educational Resources Information Center

Maher, Carolyn A.

1990-01-01

Described are engineering programs that have used automated data acquisition systems to implement data collection and analyze experiments. Applications include a biochemical engineering laboratory, heat transfer performance, engineering materials testing, mechanical system reliability, statistical control laboratory, thermo-fluid laboratory, and a…

Learning curves for single incision and conventional laparoscopic right hemicolectomy: a multidimensional analysis

PubMed Central

Park, Yoonah; Yong, Yuen Geng; Jung, Kyung Uk; Huh, Jung Wook; Cho, Yong Beom; Kim, Hee Cheol; Lee, Woo Yong; Chun, Ho-Kyung

2015-01-01

Purpose This study aimed to compare the learning curves and early postoperative outcomes for conventional laparoscopic (CL) and single incision laparoscopic (SIL) right hemicolectomy (RHC). Methods This retrospective study included the initial 35 cases in each group. Learning curves were evaluated by the moving average of operative time, mean operative time of every five consecutive cases, and cumulative sum (CUSUM) analysis. The learning phase was considered overcome when the moving average of operative times reached a plateau, and when the mean operative time of every five consecutive cases reached a low point and subsequently did not vary by more than 30 minutes. Results Six patients with missing data in the CL RHC group were excluded from the analyses. According to the mean operative time of every five consecutive cases, learning phase of SIL and CL RHC was completed between 26 and 30 cases, and 16 and 20 cases, respectively. Moving average analysis revealed that approximately 31 (SIL) and 25 (CL) cases were needed to complete the learning phase, respectively. CUSUM analysis demonstrated that 10 (SIL) and two (CL) cases were required to reach a steady state of complication-free performance, respectively. Postoperative complications rate was higher in SIL than in CL group, but the difference was not statistically significant (17.1% vs. 3.4%). Conclusion The learning phase of SIL RHC is longer than that of CL RHC. Early oncological outcomes of both techniques were comparable. However, SIL RHC had a statistically insignificant higher complication rate than CL RHC during the learning phase. PMID:25960990

Making the Grade: A Report on Standards in Work-Based Learning for Young People.

ERIC Educational Resources Information Center

Hughes, Maria

The reasons for the deterioration of the inspection grades awarded for work-based learning (WBL) provision in England were examined. The main data collection activities were as follows: (1) a statistical analysis of the Training Standards Council (TSC) and Adult Learning Inspectorate (ALI) inspection grades in 1998-2001; (2) a qualitative review…

E-Learning in Thailand: An Analysis and Case Study

ERIC Educational Resources Information Center

Suanpang, Pannee; Petocz, Peter

2006-01-01

This article presents a discussion of e-learning in the context of Thailand using as an example a study carried out in a course in Business Statistics at Suan Dusit Rajabhat University (SDU), Thailand. The online course was a pioneering research project at SDU for studying the efficiency and effectiveness of the online learning system. The…

Cognitive and Socio-Affective Outcomes of Project-Based Learning: Perceptions of Greek Second Chance School Students

ERIC Educational Resources Information Center

Koutrouba, Konstantina; Karageorgou, Elissavet

2013-01-01

The present questionnaire-based study was conducted in 2010 in order to examine 677 Greek Second Chance School (SCS) students' perceptions about the cognitive and socio-affective outcomes of project-based learning. Data elaboration, statistical and factor analysis showed that the participants found that project-based learning offered a second…

Math Academy: Play Ball! Explorations in Data Analysis & Statistics. Book 3: Supplemental Math Materials for Grades 3-8

ERIC Educational Resources Information Center

Rimbey, Kimberly

2008-01-01

Created by teachers for teachers, the Math Academy tools and activities included in this booklet were designed to create hands-on activities and a fun learning environment for the teaching of mathematics to the students. This booklet contains the "Math Academy--Play Ball! Explorations in Data Analysis & Statistics," which teachers can use to…

Statistical Optimality in Multipartite Ranking and Ordinal Regression.

PubMed

Uematsu, Kazuki; Lee, Yoonkyung

2015-05-01

Statistical optimality in multipartite ranking is investigated as an extension of bipartite ranking. We consider the optimality of ranking algorithms through minimization of the theoretical risk which combines pairwise ranking errors of ordinal categories with differential ranking costs. The extension shows that for a certain class of convex loss functions including exponential loss, the optimal ranking function can be represented as a ratio of weighted conditional probability of upper categories to lower categories, where the weights are given by the misranking costs. This result also bridges traditional ranking methods such as proportional odds model in statistics with various ranking algorithms in machine learning. Further, the analysis of multipartite ranking with different costs provides a new perspective on non-smooth list-wise ranking measures such as the discounted cumulative gain and preference learning. We illustrate our findings with simulation study and real data analysis.

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.

PubMed

Brito, Carlos S N; Gerstner, Wulfram

2016-09-01

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

PubMed Central

Gerstner, Wulfram

2016-01-01

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. PMID:27690349

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Dental hygiene students' perceptions of distance learning: do they change over time?

PubMed

Sledge, Rhonda; Vuk, Jasna; Long, Susan

2014-02-01

The University of Arkansas for Medical Sciences dental hygiene program established a distant site where the didactic curriculum was broadcast via interactive video from the main campus to the distant site, supplemented with on-line learning via Blackboard. This study compared the perceptions of students towards distance learning as they progressed through the 21 month curriculum. Specifically, the study sought to answer the following questions: Is there a difference in the initial perceptions of students on the main campus and at the distant site toward distance learning? Do students' perceptions change over time with exposure to synchronous distance learning over the course of the curriculum? All 39 subjects were women between the ages of 20 and 35 years. Of the 39 subjects, 37 were Caucasian and 2 were African-American. A 15-question Likert scale survey was administered at 4 different periods during the 21 month program to compare changes in perceptions toward distance learning as students progressed through the program. An independent sample t-test and ANOVA were utilized for statistical analysis. At the beginning of the program, independent samples t-test revealed that students at the main campus (n=34) perceived statistically significantly higher effectiveness of distance learning than students at the distant site (n=5). Repeated measures of ANOVA revealed that perceptions of students at the main campus on effectiveness and advantages of distance learning statistically significantly decreased whereas perceptions of students at distant site statistically significantly increased over time. Distance learning in the dental hygiene program was discussed, and replication of the study with larger samples of students was recommended.

Did Tanzania Achieve the Second Millennium Development Goal? Statistical Analysis

ERIC Educational Resources Information Center

Magoti, Edwin

2016-01-01

Development Goal "Achieve universal primary education", the challenges faced, along with the way forward towards achieving the fourth Sustainable Development Goal "Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all". Statistics show that Tanzania has made very promising steps…

Programs for Children with Specific Learning Disabilities. P.L. 91-230, Title VI-G Formal Final Evaluation. (Statistical Analysis of Data).

ERIC Educational Resources Information Center

Murphy, Philip J.

The paper reports the final evaluation of a program for approximately 143 learning disabled (LD) students (grades 6-to-12) from six school districts. A number of test instruments were used to evaluate student progress during the program, including the Wide Range Achievement Test (WRAT), the Durrell Analysis of Reading Difficulty, and the…

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

ERIC Educational Resources Information Center

Brinton, Christopher G.; Chiang, Mung; Jain, Shaili; Lam, Henry; Liu, Zhenming; Wong, Felix Ming Fai

2014-01-01

We study user behavior in the courses offered by a major massive online open course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education on MOOC and is done via online discussion forums, our main focus is on understanding forum activities. Two salient features of these activities drive our…

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials.

PubMed

Potter, Christine E; Wang, Tianlin; Saffran, Jenny R

2017-04-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. Copyright © 2016 Cognitive Science Society, Inc.

Second language experience facilitates statistical learning of novel linguistic materials

PubMed Central

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2016-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In the present research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, six months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, while both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. PMID:27988939

Introduction to Statistics. Learning Packages in the Policy Sciences Series, PS-26. Revised Edition.

ERIC Educational Resources Information Center

Policy Studies Associates, Croton-on-Hudson, NY.

The primary objective of this booklet is to introduce students to basic statistical skills that are useful in the analysis of public policy data. A few, selected statistical methods are presented, and theory is not emphasized. Chapter 1 provides instruction for using tables, bar graphs, bar graphs with grouped data, trend lines, pie diagrams,…

Learning Effects of an International Group Competition Project

ERIC Educational Resources Information Center

Akpinar, Murat; del Campo, Cristina; Eryarsoy, Enes

2015-01-01

This study investigates the effects of collaboration and competition on students' learning performance in a course of business statistics. The collaboration involved a simultaneously organised group competition project with analysis of real-life business problems among students. Students from the following schools participated: JAMK University of…

A Vehicle for Bivariate Data Analysis

ERIC Educational Resources Information Center

Roscoe, Matt B.

2016-01-01

Instead of reserving the study of probability and statistics for special fourth-year high school courses, the Common Core State Standards for Mathematics (CCSSM) takes a "statistics for all" approach. The standards recommend that students in grades 6-8 learn to summarize and describe data distributions, understand probability, draw…

Making Decisions with Data: Are We Environmentally Friendly?

ERIC Educational Resources Information Center

English, Lyn; Watson, Jane

2016-01-01

Statistical literacy is a vital component of numeracy. Students need to learn to critically evaluate and interpret statistical information if they are to become informed citizens. This article examines a Year 5 unit of work that uses the data collection and analysis cycle within a sustainability context.

Metrological traceability in education: A practical online system for measuring and managing middle school mathematics instruction

NASA Astrophysics Data System (ADS)

Torres Irribarra, D.; Freund, R.; Fisher, W.; Wilson, M.

2015-02-01

Computer-based, online assessments modelled, designed, and evaluated for adaptively administered invariant measurement are uniquely suited to defining and maintaining traceability to standardized units in education. An assessment of this kind is embedded in the Assessing Data Modeling and Statistical Reasoning (ADM) middle school mathematics curriculum. Diagnostic information about middle school students' learning of statistics and modeling is provided via computer-based formative assessments for seven constructs that comprise a learning progression for statistics and modeling from late elementary through the middle school grades. The seven constructs are: Data Display, Meta-Representational Competence, Conceptions of Statistics, Chance, Modeling Variability, Theory of Measurement, and Informal Inference. The end product is a web-delivered system built with Ruby on Rails for use by curriculum development teams working with classroom teachers in designing, developing, and delivering formative assessments. The online accessible system allows teachers to accurately diagnose students' unique comprehension and learning needs in a common language of real-time assessment, logging, analysis, feedback, and reporting.

Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies.

PubMed

McElreath, Richard; Bell, Adrian V; Efferson, Charles; Lubell, Mark; Richerson, Peter J; Waring, Timothy

2008-11-12

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

PubMed

Baldi, P; Sadowski, P; Whiteson, D

2015-03-20

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report.

PubMed

Kuppuraj, Sengottuvel; Duta, Mihaela; Thompson, Paul; Bishop, Dorothy

2018-02-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability ( r  = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

PubMed Central

Duta, Mihaela; Thompson, Paul

2018-01-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory–picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test–retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process. PMID:29515876

Improving Learning Performance Through Rational Resource Allocation

NASA Technical Reports Server (NTRS)

Gratch, J.; Chien, S.; DeJong, G.

1994-01-01

This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

NASA Astrophysics Data System (ADS)

Chen, Tianyi; Mokhtari, Aryan; Wang, Xin; Ribeiro, Alejandro; Giannakis, Georgios B.

2017-06-01

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Participant Interaction in Asynchronous Learning Environments: Evaluating Interaction Analysis Methods

ERIC Educational Resources Information Center

Blanchette, Judith

2012-01-01

The purpose of this empirical study was to determine the extent to which three different objective analytical methods--sequence analysis, surface cohesion analysis, and lexical cohesion analysis--can most accurately identify specific characteristics of online interaction. Statistically significant differences were found in all points of…

Using VITA Service Learning Experiences to Teach Hypothesis Testing and P-Value Analysis

ERIC Educational Resources Information Center

Drougas, Anne; Harrington, Steve

2011-01-01

This paper describes a hypothesis testing project designed to capture student interest and stimulate classroom interaction and communication. Using an online survey instrument, the authors collected student demographic information and data regarding university service learning experiences. Introductory statistics students performed a series of…

Training and Learning in the Knowledge and Service Economy

ERIC Educational Resources Information Center

Sloman, Martyn; Philpott, John

2006-01-01

Purpose: The purpose of this paper is to consider whether the shift from training to learning is related to employment categories using a categorisation popularised by Robert Reich. Design/methodology/approach: Collation and analysis of existing CIPD research information and assessment of labour statistics. Findings: An examination of the national…

The Learning Organization Model across Vocational and Academic Teacher Groups

ERIC Educational Resources Information Center

Park, Joo Ho; Rojewski, Jay W.

2006-01-01

Multiple-group confirmatory factor analysis was used to investigate factorial invariance between vocational and academic teacher groups on a measure of the learning organization concept. Participants were 488 full-time teachers of public trade industry-technical and business schools located within Seoul, South Korea. Statistically significant…

Postural-Sway Response in Learning-Disabled Children: Pilot Data.

ERIC Educational Resources Information Center

Polatajko, H. J.

1987-01-01

The postural-sway response of five learning disabled (LD) and five nondisabled children was evaluated using a force platform. From statistical analysis of the two groups, the LD children appeared to use visual input to compensate for postural problems and had significant difficulty controlling posture with eyes closed. (SK)

Prediction during statistical learning, and implications for the implicit/explicit divide

PubMed Central

Dale, Rick; Duran, Nicholas D.; Morehead, J. Ryan

2012-01-01

Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning. PMID:22723817

Different Levels of Leadership for Learning: Investigating Differences between Teachers Individually and Collectively Using Multilevel Factor Analysis of the 2011-2012 Schools and Staffing Survey

ERIC Educational Resources Information Center

Boyce, Jared; Bowers, Alex J.

2018-01-01

This study investigated the differences between how individual teachers perceive leadership for learning and how teachers collectively perceive leadership for learning, using a large nationally generalizable data-set of 7070 schools from the National Center for Education Statistics 2011-2012 Schools and Staffing Survey. This study used…

A Comparison of Classroom and Online Asynchronous Problem-Based Learning for Students Undertaking Statistics Training as Part of a Public Health Masters Degree

ERIC Educational Resources Information Center

de Jong, N.; Verstegen, D. M. L.; Tan, F. E. S.; O'Connor, S. J.

2013-01-01

This case-study compared traditional, face-to-face classroom-based teaching with asynchronous online learning and teaching methods in two sets of students undertaking a problem-based learning module in the multilevel and exploratory factor analysis of longitudinal data as part of a Masters degree in Public Health at Maastricht University. Students…

World Population: Facts in Focus. World Population Data Sheet Workbook. Population Learning Series.

ERIC Educational Resources Information Center

Crews, Kimberly A.

This workbook teaches population analysis using world population statistics. To complete the four student activity sheets, the students refer to the included "1988 World Population Data Sheet" which lists nations' statistical data that includes population totals, projected population, birth and death rates, fertility levels, and the…

Surviving an Avalanche of Data

ERIC Educational Resources Information Center

English, Lyn D.

2013-01-01

The National Council of Teachers of Mathematics (NCTM) continues to emphasize the importance of early statistical learning; data analysis and probability was the Council's professional development "Focus of the Year" for 2007-2008. Such a focus is needed, especially given the results of the statistics items from the 2003 NAEP. As…

Analyzing a Mature Software Inspection Process Using Statistical Process Control (SPC)

NASA Technical Reports Server (NTRS)

Barnard, Julie; Carleton, Anita; Stamper, Darrell E. (Technical Monitor)

1999-01-01

This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.

Explorations in statistics: the log transformation.

PubMed

Curran-Everett, Douglas

2018-06-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This thirteenth installment of Explorations in Statistics explores the log transformation, an established technique that rescales the actual observations from an experiment so that the assumptions of some statistical analysis are better met. A general assumption in statistics is that the variability of some response Y is homogeneous across groups or across some predictor variable X. If the variability-the standard deviation-varies in rough proportion to the mean value of Y, a log transformation can equalize the standard deviations. Moreover, if the actual observations from an experiment conform to a skewed distribution, then a log transformation can make the theoretical distribution of the sample mean more consistent with a normal distribution. This is important: the results of a one-sample t test are meaningful only if the theoretical distribution of the sample mean is roughly normal. If we log-transform our observations, then we want to confirm the transformation was useful. We can do this if we use the Box-Cox method, if we bootstrap the sample mean and the statistic t itself, and if we assess the residual plots from the statistical model of the actual and transformed sample observations.

Statistical learning and selective inference.

PubMed

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Dynamics of EEG functional connectivity during statistical learning.

PubMed

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

The Practicality of Statistical Physics Handout Based on KKNI and the Constructivist Approach

NASA Astrophysics Data System (ADS)

Sari, S. Y.; Afrizon, R.

2018-04-01

Statistical physics lecture shows that: 1) the performance of lecturers, social climate, students’ competence and soft skills needed at work are in enough category, 2) students feel difficulties in following the lectures of statistical physics because it is abstract, 3) 40.72% of students needs more understanding in the form of repetition, practice questions and structured tasks, and 4) the depth of statistical physics material needs to be improved gradually and structured. This indicates that learning materials in accordance of The Indonesian National Qualification Framework or Kerangka Kualifikasi Nasional Indonesia (KKNI) with the appropriate learning approach are needed to help lecturers and students in lectures. The author has designed statistical physics handouts which have very valid criteria (90.89%) according to expert judgment. In addition, the practical level of handouts designed also needs to be considered in order to be easy to use, interesting and efficient in lectures. The purpose of this research is to know the practical level of statistical physics handout based on KKNI and a constructivist approach. This research is a part of research and development with 4-D model developed by Thiagarajan. This research activity has reached part of development test at Development stage. Data collection took place by using a questionnaire distributed to lecturers and students. Data analysis using descriptive data analysis techniques in the form of percentage. The analysis of the questionnaire shows that the handout of statistical physics has very practical criteria. The conclusion of this study is statistical physics handouts based on the KKNI and constructivist approach have been practically used in lectures.

Perceptual statistical learning over one week in child speech production.

PubMed

Richtsmeier, Peter T; Goffman, Lisa

2017-07-01

What cognitive mechanisms account for the trajectory of speech sound development, in particular, gradually increasing accuracy during childhood? An intriguing potential contributor is statistical learning, a type of learning that has been studied frequently in infant perception but less often in child speech production. To assess the relevance of statistical learning to developing speech accuracy, we carried out a statistical learning experiment with four- and five-year-olds in which statistical learning was examined over one week. Children were familiarized with and tested on word-medial consonant sequences in novel words. There was only modest evidence for statistical learning, primarily in the first few productions of the first session. This initial learning effect nevertheless aligns with previous statistical learning research. Furthermore, the overall learning effect was similar to an estimate of weekly accuracy growth based on normative studies. The results implicate other important factors in speech sound development, particularly learning via production. Copyright © 2017 Elsevier Inc. All rights reserved.

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper we review several of these techniques including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We survey their existing application in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations and how common pitfalls can be avoided.

Applications of "Integrated Data Viewer'' (IDV) in the classroom

NASA Astrophysics Data System (ADS)

Nogueira, R.; Cutrim, E. M.

2006-06-01

Conventionally, weather products utilized in synoptic meteorology reduce phenomena occurring in four dimensions to a 2-dimensional form. This constitutes a road-block for non-atmospheric-science majors who need to take meteorology as a non-mathematical and complementary course to their major programs. This research examines the use of Integrated Data Viewer-IDV as a teaching tool, as it allows a 4-dimensional representation of weather products. IDV was tested in the teaching of synoptic meteorology, weather analysis, and weather map interpretation to non-science students in the laboratory sessions of an introductory meteorology class at Western Michigan University. Comparison of student exam scores according to the laboratory teaching techniques, i.e., traditional lab manual and IDV was performed for short- and long-term learning. Results of the statistical analysis show that the Fall 2004 students in the IDV-based lab session retained learning. However, in the Spring 2005 the exam scores did not reflect retention in learning when compared with IDV-based and MANUAL-based lab scores (short term learning, i.e., exam taken one week after the lab exercise). Testing the long-term learning, seven weeks between the two exams in the Spring 2005, show no statistically significant difference between IDV-based group scores and MANUAL-based group scores. However, the IDV group obtained exam score average slightly higher than the MANUAL group. Statistical testing of the principal hypothesis in this study, leads to the conclusion that the IDV-based method did not prove to be a better teaching tool than the traditional paper-based method. Future studies could potentially find significant differences in the effectiveness of both manual and IDV methods if the conditions had been more controlled. That is, students in the control group should not be exposed to the weather analysis using IDV during lecture.

Mathematics authentic assessment on statistics learning: the case for student mini projects

NASA Astrophysics Data System (ADS)

Fauziah, D.; Mardiyana; Saputro, D. R. S.

2018-03-01

Mathematics authentic assessment is a form of meaningful measurement of student learning outcomes for the sphere of attitude, skill and knowledge in mathematics. The construction of attitude, skill and knowledge achieved through the fulfilment of tasks which involve active and creative role of the students. One type of authentic assessment is student mini projects, started from planning, data collecting, organizing, processing, analysing and presenting the data. The purpose of this research is to learn the process of using authentic assessments on statistics learning which is conducted by teachers and to discuss specifically the use of mini projects to improving students’ learning in the school of Surakarta. This research is an action research, where the data collected through the results of the assessments rubric of student mini projects. The result of data analysis shows that the average score of rubric of student mini projects result is 82 with 96% classical completeness. This study shows that the application of authentic assessment can improve students’ mathematics learning outcomes. Findings showed that teachers and students participate actively during teaching and learning process, both inside and outside of the school. Student mini projects also provide opportunities to interact with other people in the real context while collecting information and giving presentation to the community. Additionally, students are able to exceed more on the process of statistics learning using authentic assessment.

Towards application of rule learning to the meta-analysis of clinical data: an example of the metabolic syndrome.

PubMed

Wojtusiak, Janusz; Michalski, Ryszard S; Simanivanh, Thipkesone; Baranova, Ancha V

2009-12-01

Systematic reviews and meta-analysis of published clinical datasets are important part of medical research. By combining results of multiple studies, meta-analysis is able to increase confidence in its conclusions, validate particular study results, and sometimes lead to new findings. Extensive theory has been built on how to aggregate results from multiple studies and arrive to the statistically valid conclusions. Surprisingly, very little has been done to adopt advanced machine learning methods to support meta-analysis. In this paper we describe a novel machine learning methodology that is capable of inducing accurate and easy to understand attributional rules from aggregated data. Thus, the methodology can be used to support traditional meta-analysis in systematic reviews. Most machine learning applications give primary attention to predictive accuracy of the learned knowledge, and lesser attention to its understandability. Here we employed attributional rules, the special form of rules that are relatively easy to interpret for medical experts who are not necessarily trained in statistics and meta-analysis. The methodology has been implemented and initially tested on a set of publicly available clinical data describing patients with metabolic syndrome (MS). The objective of this application was to determine rules describing combinations of clinical parameters used for metabolic syndrome diagnosis, and to develop rules for predicting whether particular patients are likely to develop secondary complications of MS. The aggregated clinical data was retrieved from 20 separate hospital cohorts that included 12 groups of patients with present liver disease symptoms and 8 control groups of healthy subjects. The total of 152 attributes were used, most of which were measured, however, in different studies. Twenty most common attributes were selected for the rule learning process. By applying the developed rule learning methodology we arrived at several different possible rulesets that can be used to predict three considered complications of MS, namely nonalcoholic fatty liver disease (NAFLD), simple steatosis (SS), and nonalcoholic steatohepatitis (NASH).

Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

NASA Astrophysics Data System (ADS)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-10-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

Learning the Language of Statistics: Challenges and Teaching Approaches

ERIC Educational Resources Information Center

Dunn, Peter K.; Carey, Michael D.; Richardson, Alice M.; McDonald, Christine

2016-01-01

Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer…

The Effect of Race on Self-Esteem and Depression in Learning Disabled Children.

ERIC Educational Resources Information Center

Stanley, Patricia D.; And Others

This study examined relationships between self-esteem, depression, and race in 70 learning disabled high school students (39 white and 31 black). Subjects were administered the Coopersmith Self Esteem Inventory and the Children's Depression Inventory. Statistical analysis indicated a significant sex by race interaction. Both white females and…

Impact of E-Learning and Digitalization in Primary and Secondary Schools

ERIC Educational Resources Information Center

Tunmibi, Sunday; Aregbesola, Ayooluwa; Adejobi, Pascal; Ibrahim, Olaniyi

2015-01-01

This study examines into the impact of e-learning and digitalization in primary and secondary schools, using Greensprings School in Lagos State, Nigeria as a case study. Questionnaire was used as a data collection instrument, and descriptive statistical method was adopted for analysis. Responses from students and teachers reveal that application…

Economics: A Discriminant Analysis of Students' Perceptions of Web-Based Learning.

ERIC Educational Resources Information Center

Usip, Ebenge E.; Bee, Richard H.

1998-01-01

Users and nonusers of Web-based instruction (WBI) in an undergraduate statistics classes at Youngstown State University were surveyed. Users concluded that distance learning via the Web was a good method of obtaining general information and useful tool in improving their academic performance. Nonusers thought the university should provide…

Introduction of Digital Storytelling in Preschool Education: A Case Study from Croatia

ERIC Educational Resources Information Center

Preradovic, Nives Mikelic; Lesin, Gordana; Boras, Damir

2016-01-01

Our case study from Croatia showed the benefits of digital storytelling in a preschool as a basis for the formal ICT education. The statistical analysis revealed significant differences between children aged 6-7 who learned mathematics by traditional storytelling compared to those learning through digital storytelling. The experimental group that…

Improving Learning in Primary Schools of Developing Countries: A Meta-Analysis of Randomized Experiments

ERIC Educational Resources Information Center

McEwan, Patrick J.

2015-01-01

I gathered 77 randomized experiments (with 111 treatment arms) that evaluated the effects of school-based interventions on learning in developing-country primary schools. On average, monetary grants and deworming treatments had mean effect sizes that were close to zero and not statistically significant. Nutritional treatments, treatments that…

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Machine learning patterns for neuroimaging-genetic studies in the cloud.

PubMed

Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

2014-01-01

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

Improved analyses using function datasets and statistical modeling

Treesearch

John S. Hogland; Nathaniel M. Anderson

2014-01-01

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework...

Advanced Categorical Statistics: Issues and Applications in Communication Research.

ERIC Educational Resources Information Center

Denham, Bryan E.

2002-01-01

Discusses not only the procedures, assumptions, and applications of advanced categorical statistics, but also covers some common misapplications, from which a great deal can be learned. Addresses the use and limitations of cross-tabulation and chi-square analysis, as well as issues such as observation independence and artificial inflation of a…

The Power of 'Evidence': Reliable Science or a Set of Blunt Tools?

ERIC Educational Resources Information Center

Wrigley, Terry

2018-01-01

In response to the increasing emphasis on 'evidence-based teaching', this article examines the privileging of randomised controlled trials and their statistical synthesis (meta-analysis). It also pays particular attention to two third-level statistical syntheses: John Hattie's "Visible learning" project and the EEF's "Teaching and…

Student Distractor Choices on the Mathematics Virginia Standards of Learning Middle School Assessments

ERIC Educational Resources Information Center

Lewis, Virginia Vimpeny

2011-01-01

Number Concepts; Measurement; Geometry; Probability; Statistics; and Patterns, Functions and Algebra. Procedural Errors were further categorized into the following content categories: Computation; Measurement; Statistics; and Patterns, Functions, and Algebra. The results of the analysis showed the main sources of error for 6th, 7th, and 8th…

Integrating Statistical Visualization Research into the Political Science Classroom

ERIC Educational Resources Information Center

Draper, Geoffrey M.; Liu, Baodong; Riesenfeld, Richard F.

2011-01-01

The use of computer software to facilitate learning in political science courses is well established. However, the statistical software packages used in many political science courses can be difficult to use and counter-intuitive. We describe the results of a preliminary user study suggesting that visually-oriented analysis software can help…

Supply side management innovation: —Analysis on Enterprises in Scientific Industry Park Based on Market Orientation

NASA Astrophysics Data System (ADS)

Li, Xing; Mao, Fenlan; Lin, Mian; Yadi, Nan

2017-12-01

This research presents a conceptual framework for incorporating organizational learning and innovations as the mediating variables between market orientation and organizational performance. The samples of this study include 145 companies from the information technology industry in the Scientific Industry Parks. The global model fit is acceptable. This empirical result supports the constructs mentioned above. 1. Market orientation has a positive and direct impact on organizational learning, administrative and technical innovation. 2. Organizational learning has a positive and direct impact on administrative and technical innovation, but with no statistically significant direct impact on performance. 3. Organizational learning does have a positive and indirect impact on performance by means of organizational innovations. 4. It is not statistically significant that the impact of the two innovation types (both administrative and technical) interact with each other.

AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning

NASA Astrophysics Data System (ADS)

Ivezic, Zeljko; Connolly, Andrew J.; Vanderplas, Jacob

2015-01-01

We present AstroML, a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy, and distributed under an open license. AstroML contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets (such as SDSS and other recent major surveys), and a large suite of examples of analyzing and visualizing astronomical datasets. AstroML is especially suitable for introducing undergraduate students to numerical research projects and for graduate students to rapidly undertake cutting-edge research. The long-term goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics (see http://www.astroml.org).

[Statistical analysis using freely-available "EZR (Easy R)" software].

PubMed

Kanda, Yoshinobu

2015-10-01

Clinicians must often perform statistical analyses for purposes such evaluating preexisting evidence and designing or executing clinical studies. R is a free software environment for statistical computing. R supports many statistical analysis functions, but does not incorporate a statistical graphical user interface (GUI). The R commander provides an easy-to-use basic-statistics GUI for R. However, the statistical function of the R commander is limited, especially in the field of biostatistics. Therefore, the author added several important statistical functions to the R commander and named it "EZR (Easy R)", which is now being distributed on the following website: http://www.jichi.ac.jp/saitama-sct/. EZR allows the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates and so on, by point-and-click access. In addition, by saving the script automatically created by EZR, users can learn R script writing, maintain the traceability of the analysis, and assure that the statistical process is overseen by a supervisor.

SOCR: Statistics Online Computational Resource

PubMed Central

Dinov, Ivo D.

2011-01-01

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student’s intuition and enhance their learning. PMID:21451741

A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

PubMed

Fuller, Daniel; Buote, Richard; Stanley, Kevin

2017-11-01

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Moral foundations in an interacting neural networks society: A statistical mechanics analysis

NASA Astrophysics Data System (ADS)

Vicente, R.; Susemihl, A.; Jericó, J. P.; Caticha, N.

2014-04-01

The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that the statistics of weights attributed to each moral dimension is related to self-declared political affiliation, which in turn has been connected to cognitive learning styles by the recent literature in neuroscience and psychology. Inspired by these data, we propose a simple statistical mechanics model with interacting neural networks classifying vectors and learning from members of their social neighbourhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents when disagreeing. We consider a family of learning algorithms parametrized by δ, that represents the importance given to corroborating (same sign) opinions. We define an order parameter that quantifies the diversity of opinions in a group with homogeneous learning style. Using Monte Carlo simulations and a mean field approximation we find the relation between the order parameter and the learning parameter δ at a temperature we associate with the importance of social influence in a given group. In concordance with data, groups that rely more strongly on corroborating evidence sustain less opinion diversity. We discuss predictions of the model and propose possible experimental tests.

Deep learning for media analysis in defense scenariosan evaluation of an open source framework for object detection in intelligence related image sets

DTIC Science & Technology

2017-06-01

Training time statistics from Jones’ thesis. . . . . . . . . . . . . . 15 Table 2.2 Evaluation runtime statistics from Camp’s thesis for a single image. 17...Table 2.3 Training and evaluation runtime statistics from Sharpe’s thesis. . . 19 Table 2.4 Sharpe’s screenshot detector results for combinations of...training resources available and time required for each algorithm Jones [15] tested. Table 2.1. Training time statistics from Jones’ [15] thesis. Algorithm

Self-Regulated Learning Strategies in Relation with Statistics Anxiety

ERIC Educational Resources Information Center

Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin

2011-01-01

Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

USGS Publications Warehouse

Michael, Andrew J.; Wiemer, Stefan

2010-01-01

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

Implicit Statistical Learning and Language Skills in Bilingual Children

ERIC Educational Resources Information Center

Yim, Dongsun; Rudoy, John

2013-01-01

Purpose: Implicit statistical learning in 2 nonlinguistic domains (visual and auditory) was used to investigate (a) whether linguistic experience influences the underlying learning mechanism and (b) whether there are modality constraints in predicting implicit statistical learning with age and language skills. Method: Implicit statistical learning…

Relationship between perceptual learning in speech and statistical learning in younger and older adults

PubMed Central

Neger, Thordis M.; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly. PMID:25225475

Relationship between perceptual learning in speech and statistical learning in younger and older adults.

PubMed

Neger, Thordis M; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly.

The Mediating Role of Organizational Learning in the Relationship of Organizational Intelligence and Organizational Agility.

PubMed

Bahrami, Mohammad Amin; Kiani, Mohammad Mehdi; Montazeralfaraj, Raziye; Zadeh, Hossein Fallah; Zadeh, Morteza Mohammad

2016-06-01

Organizational learning is defined as creating, absorbing, retaining, transferring, and application of knowledge within an organization. This article aims to examine the mediating role of organizational learning in the relationship of organizational intelligence and organizational agility. This analytical and cross-sectional study was conducted in 2015 at four teaching hospitals of Yazd city, Iran. A total of 370 administrative and medical staff contributed to the study. We used stratified-random method for sampling. Required data were gathered using three valid questionnaires including Alberkht (2003) organizational intelligence, Neefe (2001) organizational learning, and Sharifi and Zhang (1999) organizational agility questionnaires. Data analysis was done through R and SPSS 18 statistical software. The results showed that organizational learning acts as a mediator in the relationship of organizational intelligence and organizational agility (path coefficient = 0.943). Also, organizational learning has a statistical relationship with organizational agility (path coefficient = 0.382). Our findings suggest that the improvement of organizational learning abilities can affect an organization's agility which is crucial for its survival.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

ERIC Educational Resources Information Center

Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

2018-01-01

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

Statistical Learning Is Related to Early Literacy-Related Skills

ERIC Educational Resources Information Center

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from…

Dissecting Sequences of Regulation and Cognition: Statistical Discourse Analysis of Primary School Children's Collaborative Learning

ERIC Educational Resources Information Center

Molenaar, Inge; Chiu, Ming Ming

2014-01-01

Extending past research showing that regulative activities (metacognitive and relational) can aid learning, this study tests whether sequences of cognitive, metacognitive and relational activities affect subsequent cognition. Scaffolded by a computer avatar, 54 primary school students (working in 18 groups of 3) discussed writing a report about a…

Combining Self-Explaining with Computer Architecture Diagrams to Enhance the Learning of Assembly Language Programming

ERIC Educational Resources Information Center

Hung, Y.-C.

2012-01-01

This paper investigates the impact of combining self explaining (SE) with computer architecture diagrams to help novice students learn assembly language programming. Pre- and post-test scores for the experimental and control groups were compared and subjected to covariance (ANCOVA) statistical analysis. Results indicate that the SE-plus-diagram…

An In-Depth Analysis of Adult Learning Policies and Their Effectiveness in Europe

ERIC Educational Resources Information Center

European Union, 2015

2015-01-01

Adult learning policies, like any other policies, need to be effective: they need to reach their objectives and attain the desired impacts, which should be carefully defined. Understanding the performance of policies allows policy makers to change and improve them. A growing body of research and statistics provides important insights into how…

Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses

ERIC Educational Resources Information Center

Galbraith, Craig S.; Merrill, Gregory B.; Kline, Doug M.

2012-01-01

In this study we investigate the underlying relational structure between student evaluations of teaching effectiveness (SETEs) and achievement of student learning outcomes in 116 business related courses. Utilizing traditional statistical techniques, a neural network analysis and a Bayesian data reduction and classification algorithm, we find…

A Statistical Analysis of Education Service Quality Dimensions on Business School Students' Satisfaction

ERIC Educational Resources Information Center

Seng, Ernest Lim Kok; Ling, Tan Pei

2013-01-01

This study aims to investigate student satisfaction on quality education services provided by institutions of higher learning in Malaysia. Their level of satisfaction based primarily on the data collected through five dimensions of education service quality. A random sample of 250 students studying in an institution of higher learning was selected…

Reflective Outcomes of Convergent and Divergent Group Tasking in the Online Learning Environment

ERIC Educational Resources Information Center

Hawkes, Mark

2007-01-01

Using collaborative critical reflection as an index, this study examines the asynchronous and face-to-face discourse of 28 suburban Chicago elementary teachers developing problem based learning (PBL) curriculum. Statistical analysis of the corpus produced by the 2 mediums shows that the asynchronous online network emerges as the medium of choice…

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Helping Students Develop Statistical Reasoning: Implementing a Statistical Reasoning Learning Environment

ERIC Educational Resources Information Center

Garfield, Joan; Ben-Zvi, Dani

2009-01-01

This article describes a model for an interactive, introductory secondary- or tertiary-level statistics course that is designed to develop students' statistical reasoning. This model is called a "Statistical Reasoning Learning Environment" and is built on the constructivist theory of learning.

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

PubMed

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-07-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

An Automated Statistical Process Control Study of Inline Mixing Using Spectrophotometric Detection

ERIC Educational Resources Information Center

Dickey, Michael D.; Stewart, Michael D.; Willson, C. Grant

2006-01-01

An experiment is described, which is designed for a junior-level chemical engineering "fundamentals of measurements and data analysis" course, where students are introduced to the concept of statistical process control (SPC) through a simple inline mixing experiment. The students learn how to create and analyze control charts in an effort to…

Measuring the Effects of Peer Learning on Students' Academic Achievement in First-Year Business Statistics

ERIC Educational Resources Information Center

Dancer, Diane; Morrison, Kellie; Tarr, Garth

2015-01-01

Peer-assisted study session (PASS) programs have been shown to positively affect students' grades in a majority of studies. This study extends that analysis in two ways: controlling for ability and other factors, with focus on international students, and by presenting results for PASS in business statistics. Ordinary least squares, random effects…

The Effect of Using Case Studies in Business Statistics

ERIC Educational Resources Information Center

Pariseau, Susan E.; Kezim, Boualem

2007-01-01

The authors evaluated the effect on learning of using case studies in business statistics courses. The authors divided students into 3 groups: a control group, a group that completed 1 case study, and a group that completed 3 case studies. Results evidenced that, on average, students whom the authors required to complete a case analysis received…

A Role for Chunk Formation in Statistical Learning of Second Language Syntax

ERIC Educational Resources Information Center

Hamrick, Phillip

2014-01-01

Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by…

Statistical Learning is Related to Early Literacy-Related Skills

PubMed Central

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed. PMID:26478658

Comparing associative, statistical, and inferential reasoning accounts of human contingency learning.

PubMed

Pineño, Oskar; Miller, Ralph R

2007-03-01

For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years.

A Study of the Effectiveness of the Contextual Lab Activity in the Teaching and Learning Statistics at the UTHM (Universiti Tun Hussein Onn Malaysia)

ERIC Educational Resources Information Center

Kamaruddin, Nafisah Kamariah Md; Jaafar, Norzilaila bt; Amin, Zulkarnain Md

2012-01-01

Inaccurate concept in statistics contributes to the assumption by the students that statistics do not relate to the real world and are not relevant to the engineering field. There are universities which introduced learning statistics using statistics lab activities. However, the learning is more on the learning how to use software and not to…

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Clinical learning environment and supervision: experiences of Norwegian nursing students - a questionnaire survey.

PubMed

Skaalvik, Mari Wolff; Normann, Hans Ketil; Henriksen, Nils

2011-08-01

To measure nursing students' experiences and satisfaction with their clinical learning environments. The primary interest was to compare the results between students with respect to clinical practice in nursing homes and hospital wards. Clinical learning environments are important for the learning processes of nursing students and for preferences for future workplaces. Working with older people is the least preferred area of practice among nursing students in Norway. A cross-sectional design. A validated questionnaire was distributed to all nursing students from five non-randomly selected university colleges in Norway. A total of 511 nursing students completed a Norwegian version of the questionnaire, Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) evaluation scale in 2009. Data including descriptive statistics were analysed using the Statistical Program for the Social Sciences. Factor structure was analysed by principal component analysis. Differences across sub-groups were tested with chi-square tests and Mann-Whitney U test for categorical variables and t-tests for continuous variables. Ordinal logistic regression analysis of perceptions of the ward as a good learning environment was performed with supervisory relationships and institutional contexts as independent variables, controlling for age, sex and study year. The participating nursing students with clinical placements in nursing homes assessed their clinical learning environment significantly more negatively than those with hospital placements on nearby all sub-dimensions. The evidence found in this study indicates that measures should be taken to strengthen nursing homes as learning environments for nursing students. To recruit more graduated nurses to work in nursing homes, actions to improve the learning environment are needed. © 2011 Blackwell Publishing Ltd.

Effect of moderate learning style–teaching mode mismatch on academic performance among 2nd year medical students in Pakistan

PubMed Central

Hamza, Muhammad; Inam-Ul-Haq; Hamid, Sidra; Nadir, Maha; Mehmood, Nadir

2018-01-01

Introduction: The vagueness surrounding “learning style–teaching mode mismatch” makes its effects uncertain. This study tried to tackle that controversy by comparing and assessing the effect of different learning styles on performance in physiology examination when teaching mode was somewhat different than learning preferences of the 2nd year medical students. Methods: A total of 102 2nd year medical students participated in this study. Honey and Mumford learning style questionnaire was used to categorize the participants into one of the four learning styles (activist, reflector, theorist, and pragmatist). Many teaching modes were used in the medical college. The first professional theory and practical physiology scores of these 102 students of University of Health Sciences were obtained online. Learning styles were compared with physiology scores and age using one-way analysis of variance and post hoc statistical analysis and between males and females by using Chi-square test. Results: Pragmatists had the lowest total physiology score (P < 0.001), while theorists had the highest total physiology scores (P < 0.001). Activists and reflectors had scores in between pragmatists and theorists, and there was no statistical difference between these two styles of learning (P = 0.9). No student scored below 60%. Conclusion: This study demonstrated that the effect of moderate teaching–learning mismatch is different for different learners. Theorists excelled as they had the highest physiology score, while pragmatists lagged in comparison. Reflectors and activists performed better than pragmatists but were worse than theorists. Despite this, none of the students scored below 60%. This shows that a moderate learning style–teaching mode mismatch is not harmful for learning. PMID:29736072

Effect of moderate learning style-teaching mode mismatch on academic performance among 2nd year medical students in Pakistan.

PubMed

Hamza, Muhammad; Inam-Ul-Haq; Hamid, Sidra; Nadir, Maha; Mehmood, Nadir

2018-01-01

The vagueness surrounding "learning style-teaching mode mismatch" makes its effects uncertain. This study tried to tackle that controversy by comparing and assessing the effect of different learning styles on performance in physiology examination when teaching mode was somewhat different than learning preferences of the 2 nd year medical students. A total of 102 2 nd year medical students participated in this study. Honey and Mumford learning style questionnaire was used to categorize the participants into one of the four learning styles (activist, reflector, theorist, and pragmatist). Many teaching modes were used in the medical college. The first professional theory and practical physiology scores of these 102 students of University of Health Sciences were obtained online. Learning styles were compared with physiology scores and age using one-way analysis of variance and post hoc statistical analysis and between males and females by using Chi-square test. Pragmatists had the lowest total physiology score ( P < 0.001), while theorists had the highest total physiology scores ( P < 0.001). Activists and reflectors had scores in between pragmatists and theorists, and there was no statistical difference between these two styles of learning ( P = 0.9). No student scored below 60%. This study demonstrated that the effect of moderate teaching-learning mismatch is different for different learners. Theorists excelled as they had the highest physiology score, while pragmatists lagged in comparison. Reflectors and activists performed better than pragmatists but were worse than theorists. Despite this, none of the students scored below 60%. This shows that a moderate learning style-teaching mode mismatch is not harmful for learning.

An exploratory study of the relationship between learning styles and academic performance among students in different nursing programs.

PubMed

Li, Yuh-Shiow; Yu, Wen-Pin; Liu, Chin-Fang; Shieh, Sue-Heui; Yang, Bao-Huan

2014-01-01

Abstract Background: Learning style is a major consideration in planning for effective and efficient instruction and learning. Learning style has been shown to influence academic performance in the previous research. Little is known about Taiwanese students' learning styles, particularly in the field of nursing education. This purpose of this study was to identify the relationship between learning styles and academic performance among nursing students in a 5-year associate degree of nursing (ADN) program and a 2-year bachelor of science in nursing (BSN) program in Taiwan. This study employed a descriptive and exploratory design. The Chinese version of the Myers-Briggs type indicator Form M was an instrument. Data such as grade point average were obtained from the Office of Academic Affairs and the Registrar computerized records. Descriptive statistics, one-way analysis of variance and chi-square statistical analysis were used to explore the relationship between academic performance and learning style in Taiwanese nursing students. The study sample included 285 nursing students: 96 students in a 2-year BSN program, and 189 students in a 5-year ADN program. Two common learning styles were found: Introversion, sensing, thinking, and judging; and introversion, sensing, feeling, and judging. A sensing-judging pair was identified in 43.3% of the participants. Academic performance was significantly related to learning style (p < 0.05, df = 15). The results of this study can help educators devise classroom and clinical instructional strategies that respond to individual needs in order to maximize academic performance and enhance student success. A large sample is recommended for further research. Understanding the learning style preferences of students can enhance learning for those who are under performing in their academic studies, thereby enhancing nursing education.

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

ERIC Educational Resources Information Center

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2017-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning…

Regression Analysis: Instructional Resource for Cost/Managerial Accounting

ERIC Educational Resources Information Center

Stout, David E.

2015-01-01

This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…

Learning curve evaluation using cumulative summation analysis-a clinical example of pediatric robot-assisted laparoscopic pyeloplasty.

PubMed

Cundy, Thomas P; Gattas, Nicholas E; White, Alan D; Najmaldin, Azad S

2015-08-01

The cumulative summation (CUSUM) method for learning curve analysis remains under-utilized in the surgical literature in general, and is described in only a small number of publications within the field of pediatric surgery. This study introduces the CUSUM analysis technique and applies it to evaluate the learning curve for pediatric robot-assisted laparoscopic pyeloplasty (RP). Clinical data were prospectively recorded for consecutive pediatric RP cases performed by a single-surgeon. CUSUM charts and tests were generated for set-up time, docking time, console time, operating time, total operating room time, and postoperative complications. Conversions and avoidable operating room delay were separately evaluated with respect to case experience. Comparisons between case experience and time-based outcomes were assessed using the Student's t-test and ANOVA for bi-phasic and multi-phasic learning curves respectively. Comparison between case experience and complication frequency was assessed using the Kruskal-Wallis test. A total of 90 RP cases were evaluated. The learning curve transitioned beyond the learning phase at cases 10, 15, 42, 57, and 58 for set-up time, docking time, console time, operating time, and total operating room time respectively. All comparisons of mean operating times between the learning phase and subsequent phases were statistically significant (P=<0.001-0.01). No significant difference was observed between case experience and frequency of post-operative complications (P=0.125), although the CUSUM chart demonstrated a directional change in slope for the last 12 cases in which there were high proportions of re-do cases and patients <6 months of age. The CUSUM method has a valuable role for learning curve evaluation and outcome quality monitoring. In applying this statistical technique to the largest reported single surgeon series of pediatric RP, we demonstrate numerous distinctly shaped learning curves and well-defined learning phase transition points. Copyright © 2015 Elsevier Inc. All rights reserved.

Machine Learning Methods for Production Cases Analysis

NASA Astrophysics Data System (ADS)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

Undergraduate medical student's perceptions on traditional and problem based curricula: pilot study.

PubMed

Meo, Sultan Ayoub

2014-07-01

To evaluate and compare students' perceptions about teaching and learning, knowledge and skills, outcomes of course materials and their satisfaction in traditional Lecture Based learning versus Problem-Based Learning curricula in two different medical schools. The comparative cross-sectional questionnaire-based study was conducted in the Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, from July 2009 to January 2011. Two different undergraduate medical schools were selected; one followed the traditional curriculum, while the other followed the problem-based learning curriculum. Two equal groups of first year medical students were selected. They were taught in respiratory physiology and lung function lab according to their curriculum for a period of two weeks. At the completion of the study period, a five-point Likert scale was used to assess students' perceptions on satisfaction, academic environment, teaching and learning, knowledge and skills and outcomes of course materials about effectiveness of problem-based learning compared to traditional methods. SPSS 19 was used for statistical analysis. Students used to problem-based learning curriculum obtained marginally higher scores in their perceptions (24.10 +/- 3.63) compared to ones following the traditional curriculum (22.67 +/- 3.74). However, the difference in perceptions did not achieve a level of statistical significance. Students following problem-based learning curriculum have more positive perceptions on teaching and learning, knowledge and skills, outcomes of their course materials and satisfaction compared to the students belonging to the traditional style of medical school. However, the difference between the two groups was not statistically significant.

Using Cluster Analysis for Data Mining in Educational Technology Research

ERIC Educational Resources Information Center

Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

2012-01-01

Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

The unrealized promise of infant statistical word-referent learning

PubMed Central

Smith, Linda B.; Suanda, Sumarga H.; Yu, Chen

2014-01-01

Recent theory and experiments offer a new solution as to how infant learners may break into word learning, by using cross-situational statistics to find the underlying word-referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word-referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics. PMID:24637154

Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets

NASA Astrophysics Data System (ADS)

Goel, Amit; Montgomery, Michele

2015-08-01

Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.

Robotic partial nephrectomy shortens warm ischemia time, reducing suturing time kinetics even for an experienced laparoscopic surgeon: a comparative analysis.

PubMed

Faria, Eliney F; Caputo, Peter A; Wood, Christopher G; Karam, Jose A; Nogueras-González, Graciela M; Matin, Surena F

2014-02-01

Laparoscopic and robotic partial nephrectomy (LPN and RPN) are strongly related to influence of tumor complexity and learning curve. We analyzed a consecutive experience between RPN and LPN to discern if warm ischemia time (WIT) is in fact improved while accounting for these two confounding variables and if so by which particular aspect of WIT. This is a retrospective analysis of consecutive procedures performed by a single surgeon between 2002-2008 (LPN) and 2008-2012 (RPN). Specifically, individual steps, including tumor excision, suturing of intrarenal defect, and parenchyma, were recorded at the time of surgery. Multivariate and univariate analyzes were used to evaluate influence of learning curve, tumor complexity, and time kinetics of individual steps during WIT, to determine their influence in WIT. Additionally, we considered the effect of RPN on the learning curve. A total of 146 LPNs and 137 RPNs were included. Considering renal function, WIT, suturing time, renorrhaphy time were found statistically significant differences in favor of RPN (p < 0.05). In the univariate analysis, surgical procedure, learning curve, clinical tumor size, and RENAL nephrometry score were statistically significant predictors for WIT (p < 0.05). RPN decreased the WIT on average by approximately 7 min compared to LPN even when adjusting for learning curve, tumor complexity, and both together (p < 0.001). We found RPN was associated with a shorter WIT when controlling for influence of the learning curve and tumor complexity. The time required for tumor excision was not shortened but the time required for suturing steps was significantly shortened.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory.

PubMed

Batista-Foguet, Joan M; Ferrer-Rosell, Berta; Serlavós, Ricard; Coenders, Germà; Boyatzis, Richard E

2015-01-01

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines-compositional data analysis (CODA) and log ratio transformations-KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory

PubMed Central

Batista-Foguet, Joan M.; Ferrer-Rosell, Berta; Serlavós, Ricard; Coenders, Germà; Boyatzis, Richard E.

2015-01-01

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines—compositional data analysis (CODA) and log ratio transformations—KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension. PMID:26617561

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Potential Values of Incorporating a Multiple-Choice Question Construction in Physics Experimentation Instruction

NASA Astrophysics Data System (ADS)

Yu, Fu-Yun; Liu, Yu-Hsin

2005-09-01

The potential value of a multiple-choice question-construction instructional strategy for the support of students’ learning of physics experiments was examined in the study. Forty-two university freshmen participated in the study for a whole semester. A constant comparison method adopted to categorize students’ qualitative data indicated that the influences of multiple-choice question construction were evident in several significant ways (promoting constructive and productive studying habits; reflecting and previewing course-related materials; increasing in-group communication and interaction; breaking passive learning style and habits, etc.), which, worked together, not only enhanced students’ comprehension and retention of the obtained knowledge, but also helped distil a sense of empowerment and learning community within the participants. Analysis with one-group t-tests, using 3 as the expected mean, on quantitative data further found that students’ satisfaction toward past learning experience, and perceptions toward this strategy’s potentials for promoting learning were statistically significant at the 0.0005 level, while learning anxiety was not statistically significant. Suggestions for incorporating question-generation activities within classroom and topics for future studies were rendered.

Is Statistical Learning Constrained by Lower Level Perceptual Organization?

PubMed Central

Emberson, Lauren L.; Liu, Ran; Zevin, Jason D.

2013-01-01

In order for statistical information to aid in complex developmental processes such as language acquisition, learning from higher-order statistics (e.g. across successive syllables in a speech stream to support segmentation) must be possible while perceptual abilities (e.g. speech categorization) are still developing. The current study examines how perceptual organization interacts with statistical learning. Adult participants were presented with multiple exemplars from novel, complex sound categories designed to reflect some of the spectral complexity and variability of speech. These categories were organized into sequential pairs and presented such that higher-order statistics, defined based on sound categories, could support stream segmentation. Perceptual similarity judgments and multi-dimensional scaling revealed that participants only perceived three perceptual clusters of sounds and thus did not distinguish the four experimenter-defined categories, creating a tension between lower level perceptual organization and higher-order statistical information. We examined whether the resulting pattern of learning is more consistent with statistical learning being “bottom-up,” constrained by the lower levels of organization, or “top-down,” such that higher-order statistical information of the stimulus stream takes priority over the perceptual organization, and perhaps influences perceptual organization. We consistently find evidence that learning is constrained by perceptual organization. Moreover, participants generalize their learning to novel sounds that occupy a similar perceptual space, suggesting that statistical learning occurs based on regions of or clusters in perceptual space. Overall, these results reveal a constraint on learning of sound sequences, such that statistical information is determined based on lower level organization. These findings have important implications for the role of statistical learning in language acquisition. PMID:23618755

Community Learning Approach (CLA) for Literacy Promotion of Women in the Fishing Villages of Region I (Philippines).

ERIC Educational Resources Information Center

Manlongat, Sylvia

After an analysis of 1990 Philippines National Statistics Office data showed a high incidence of illiteracy among women in the fishing villages, a project, Community Learning Approach (CLA), was developed to raise the literacy level. It was designed as an alternative delivery system of educating women in 24 villages for functional literacy and…

Distinct contributions of attention and working memory to visual statistical learning and ensemble processing.

PubMed

Hall, Michelle G; Mattingley, Jason B; Dux, Paul E

2015-08-01

The brain exploits redundancies in the environment to efficiently represent the complexity of the visual world. One example of this is ensemble processing, which provides a statistical summary of elements within a set (e.g., mean size). Another is statistical learning, which involves the encoding of stable spatial or temporal relationships between objects. It has been suggested that ensemble processing over arrays of oriented lines disrupts statistical learning of structure within the arrays (Zhao, Ngo, McKendrick, & Turk-Browne, 2011). Here we asked whether ensemble processing and statistical learning are mutually incompatible, or whether this disruption might occur because ensemble processing encourages participants to process the stimulus arrays in a way that impedes statistical learning. In Experiment 1, we replicated Zhao and colleagues' finding that ensemble processing disrupts statistical learning. In Experiments 2 and 3, we found that statistical learning was unimpaired by ensemble processing when task demands necessitated (a) focal attention to individual items within the stimulus arrays and (b) the retention of individual items in working memory. Together, these results are consistent with an account suggesting that ensemble processing and statistical learning can operate over the same stimuli given appropriate stimulus processing demands during exposure to regularities. (c) 2015 APA, all rights reserved).

Scaling up to address data science challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wendelberger, Joanne R.

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Scaling up to address data science challenges

DOE PAGES

Wendelberger, Joanne R.

2017-04-27

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

PubMed Central

2017-01-01

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274, 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105, 2745–2750; Thiessen & Yee 2010 Child Development 81, 1287–1303; Saffran 2002 Journal of Memory and Language 47, 172–196; Misyak & Christiansen 2012 Language Learning 62, 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39, 246–263; Thiessen et al. 2013 Psychological Bulletin 139, 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37, 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'. PMID:27872374

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

PubMed

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Understanding evaluation of learning support in mathematics and statistics

NASA Astrophysics Data System (ADS)

MacGillivray, Helen; Croft, Tony

2011-03-01

With rapid and continuing growth of learning support initiatives in mathematics and statistics found in many parts of the world, and with the likelihood that this trend will continue, there is a need to ensure that robust and coherent measures are in place to evaluate the effectiveness of these initiatives. The nature of learning support brings challenges for measurement and analysis of its effects. After briefly reviewing the purpose, rationale for, and extent of current provision, this article provides a framework for those working in learning support to think about how their efforts can be evaluated. It provides references and specific examples of how workers in this field are collecting, analysing and reporting their findings. The framework is used to structure evaluation in terms of usage of facilities, resources and services provided, and also in terms of improvements in performance of the students and staff who engage with them. Very recent developments have started to address the effects of learning support on the development of deeper approaches to learning, the affective domain and the development of communities of practice of both learners and teachers. This article intends to be a stimulus to those who work in mathematics and statistics support to gather even richer, more valuable, forms of data. It provides a 'toolkit' for those interested in evaluation of learning support and closes by referring to an on-line resource being developed to archive the growing body of evidence.

Group Blogs as Toolkits to Support Learning Environments in Statistics Subject: A Qualitative Case Study

ERIC Educational Resources Information Center

Hashim, Mohamad Hisyam Mohd

2012-01-01

In this paper, we describe the introduction of blogs to a class of Masters in Technical and Vocational Education students taking the MBE 1223 Statistics in Education module in Universiti Tun Hussein Onn Malaysia (UTHM). The purpose of the analysis is to elaborate on the perception of the participants towards blogs before, during and after training…

Musicians' edge: A comparison of auditory processing, cognitive abilities and statistical learning.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Demuth, Katherine; Arciuli, Joanne

2016-12-01

It has been hypothesized that musical expertise is associated with enhanced auditory processing and cognitive abilities. Recent research has examined the relationship between musicians' advantage and implicit statistical learning skills. In the present study, we assessed a variety of auditory processing skills, cognitive processing skills, and statistical learning (auditory and visual forms) in age-matched musicians (N = 17) and non-musicians (N = 18). Musicians had significantly better performance than non-musicians on frequency discrimination, and backward digit span. A key finding was that musicians had better auditory, but not visual, statistical learning than non-musicians. Performance on the statistical learning tasks was not correlated with performance on auditory and cognitive measures. Musicians' superior performance on auditory (but not visual) statistical learning suggests that musical expertise is associated with an enhanced ability to detect statistical regularities in auditory stimuli. Copyright © 2016 Elsevier B.V. All rights reserved.

A rational model of function learning.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Williams, Joseph J; Kalish, Michael L

2015-10-01

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.

Explorations in Statistics: Hypothesis Tests and P Values

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of "Explorations in Statistics" delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what…

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum

PubMed Central

Milic, Natasa M.; Trajkovic, Goran Z.; Bukumiric, Zoran M.; Cirkovic, Andja; Nikolic, Ivan M.; Milin, Jelena S.; Milic, Nikola V.; Savic, Marko D.; Corac, Aleksandar M.; Marinkovic, Jelena M.; Stanisavljevic, Dejana M.

2016-01-01

Background Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. Methods This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Results Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). Conclusion This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics. PMID:26859832

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum.

PubMed

Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M

2016-01-01

Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.

Statistical Learning Is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the Least)

PubMed Central

Emberson, Lauren L.; Rubinstein, Dani

2016-01-01

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. PMID:27139779

[Development of a multimedia learning DM diet education program using standardized patients and analysis of its effects on clinical competency and learning satisfaction for nursing students].

PubMed

Hyun, Kyung Sun; Kang, Hyun Sook; Kim, Won Ock; Park, Sunhee; Lee, Jia; Sok, Sohyune

2009-04-01

The purpose of this study was to develop a multimedia learning program for patients with diabetes mellitus (DM) diet education using standardized patients and to examine the effects of the program on educational skills, communication skills, DM diet knowledge and learning satisfaction. The study employed a randomized control posttest non-synchronized design. The participants were 108 third year nursing students (52 experimental group, 56 control group) at K university in Seoul, Korea. The experimental group had regular lectures and the multimedia learning program for DM diet education using standardized patients while the control group had regular lectures only. The DM educational skills were measured by trained research assistants. The students who received the multimedia learning program scored higher for DM diet educational skills, communication skills and DM diet knowledge compared to the control group. Learning satisfaction of the experimental group was higher than the control group, but statistically insignificant. Clinical competency was improved for students receiving the multimedia learning program for DM diet education using standardized patients, but there was no statistically significant effect on learning satisfaction. In the nursing education system there is a need to develop and apply more multimedia materials for education and to use standardized patients effectively.

Online neural monitoring of statistical learning

PubMed Central

Batterink, Laura J.; Paller, Ken A.

2017-01-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the RT task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. PMID:28324696

Teaching meta-analysis using MetaLight.

PubMed

Thomas, James; Graziosi, Sergio; Higgins, Steve; Coe, Robert; Torgerson, Carole; Newman, Mark

2012-10-18

Meta-analysis is a statistical method for combining the results of primary studies. It is often used in systematic reviews and is increasingly a method and topic that appears in student dissertations. MetaLight is a freely available software application that runs simple meta-analyses and contains specific functionality to facilitate the teaching and learning of meta-analysis. While there are many courses and resources for meta-analysis available and numerous software applications to run meta-analyses, there are few pieces of software which are aimed specifically at helping those teaching and learning meta-analysis. Valuable teaching time can be spent learning the mechanics of a new software application, rather than on the principles and practices of meta-analysis. We discuss ways in which the MetaLight tool can be used to present some of the main issues involved in undertaking and interpreting a meta-analysis. While there are many software tools available for conducting meta-analysis, in the context of a teaching programme such software can require expenditure both in terms of money and in terms of the time it takes to learn how to use it. MetaLight was developed specifically as a tool to facilitate the teaching and learning of meta-analysis and we have presented here some of the ways it might be used in a training situation.

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Statistical learning and language acquisition

PubMed Central

Romberg, Alexa R.; Saffran, Jenny R.

2011-01-01

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. PMID:21666883

Learning Across Senses: Cross-Modal Effects in Multisensory Statistical Learning

PubMed Central

Mitchel, Aaron D.; Weiss, Daniel J.

2014-01-01

It is currently unknown whether statistical learning is supported by modality-general or modality-specific mechanisms. One issue within this debate concerns the independence of learning in one modality from learning in other modalities. In the present study, the authors examined the extent to which statistical learning across modalities is independent by simultaneously presenting learners with auditory and visual streams. After establishing baseline rates of learning for each stream independently, they systematically varied the amount of audiovisual correspondence across 3 experiments. They found that learners were able to segment both streams successfully only when the boundaries of the audio and visual triplets were in alignment. This pattern of results suggests that learners are able to extract multiple statistical regularities across modalities provided that there is some degree of cross-modal coherence. They discuss the implications of their results in light of recent claims that multisensory statistical learning is guided by modality-independent mechanisms. PMID:21574745

Infant Statistical Learning

PubMed Central

Saffran, Jenny R.; Kirkham, Natasha Z.

2017-01-01

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

Learning Statistics at the Farmers Market? A Comparison of Academic Service Learning and Case Studies in an Introductory Statistics Course

ERIC Educational Resources Information Center

Hiedemann, Bridget; Jones, Stacey M.

2010-01-01

We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: students' performance on the final…

Changing viewer perspectives reveals constraints to implicit visual statistical learning.

PubMed

Jiang, Yuhong V; Swallow, Khena M

2014-10-07

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.

AstroML: Python-powered Machine Learning for Astronomy

NASA Astrophysics Data System (ADS)

Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

2014-01-01

As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

Functional Differences between Statistical Learning with and without Explicit Training

ERIC Educational Resources Information Center

Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…

Statistically Modeling Individual Students' Learning over Successive Collaborative Practice Opportunities

ERIC Educational Resources Information Center

Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol

2017-01-01

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…

Statistical Learning as a Key to Cracking Chinese Orthographic Codes

ERIC Educational Resources Information Center

He, Xinjie; Tong, Xiuli

2017-01-01

This study examines statistical learning as a mechanism for Chinese orthographic learning among children in Grades 3-5. Using an artificial orthography, children were repeatedly exposed to positional, phonetic, and semantic regularities of radicals. Children showed statistical learning of all three regularities. Regularities' levels of consistency…

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the…

Education on electrical phenomena involved in electroporation-based therapies and treatments: a blended learning approach.

PubMed

Čorović, Selma; Mahnič-Kalamiza, Samo; Miklavčič, Damijan

2016-04-07

Electroporation-based applications require multidisciplinary expertise and collaboration of experts with different professional backgrounds in engineering and science. Beginning in 2003, an international scientific workshop and postgraduate course electroporation based technologies and treatments (EBTT) has been organized at the University of Ljubljana to facilitate transfer of knowledge from leading experts to researches, students and newcomers in the field of electroporation. In this paper we present one of the integral parts of EBTT: an e-learning practical work we developed to complement delivery of knowledge via lectures and laboratory work, thus providing a blended learning approach on electrical phenomena involved in electroporation-based therapies and treatments. The learning effect was assessed via a pre- and post e-learning examination test composed of 10 multiple choice questions (i.e. items). The e-learning practical work session and both of the e-learning examination tests were carried out after the live EBTT lectures and other laboratory work. Statistical analysis was performed to compare and evaluate the learning effect measured in two groups of students: (1) electrical engineers and (2) natural scientists (i.e. medical doctors, biologists and chemists) undergoing the e-learning practical work in 2011-2014 academic years. Item analysis was performed to assess the difficulty of each item of the examination test. The results of our study show that the total score on the post examination test significantly improved and the item difficulty in both experimental groups decreased. The natural scientists reached the same level of knowledge (no statistical difference in total post-examination test score) on the post-course test take, as do electrical engineers, although the engineers started with statistically higher total pre-test examination score, as expected. The main objective of this study was to investigate whether the educational content the e-learning practical work presented to the students with different professional backgrounds enhanced their knowledge acquired via lectures during EBTT. We compared the learning effect assessed in two experimental groups undergoing the e-learning practical work: electrical engineers and natural scientists. The same level of knowledge on the post-course examination was reached in both groups. The results indicate that our e-learning platform supported by blended learning approach provides an effective learning tool for populations with mixed professional backgrounds and thus plays an important role in bridging the gap between scientific domains involved in electroporation-based technologies and treatments.

Decision Process to Identify Lessons for Transition to a Distributed (or Blended) Learning Instructional Format

DTIC Science & Technology

2009-09-01

instructional format. Using a mixed- method coding and analysis approach, the sample of POIs were categorized, coded, statistically analyzed, and a... Method SECURITY CLASSIFICATION OF 19. LIMITATION OF 20. NUMBER 21. RESPONSIBLE PERSON 16. REPORT Unclassified 17. ABSTRACT...transition to a distributed (or blended) learning format. Procedure: A mixed- methods approach, combining qualitative coding procedures with basic

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Pre- and In-Service Teachers' Perceptions of Learning through Research Workshops in a Hispanic-Serving Institution in USA: A Review of Two Years' Accumulative Data

ERIC Educational Resources Information Center

Lu, Ming-Tsan P.; Shin, Yousun; Overton, Terry

2016-01-01

The purpose of the study was to report the analysis results of two-year accumulative data from Research Academy workshops held for pre-service and in-service teachers in a southern state Hispanic-Serving Institution. Graduate students' perceptions of learning through these professional development workshops were reported. Statistical analyses were…

Working Towards a Scalable Model of Problem-Based Learning Instruction in Undergraduate Engineering Education

ERIC Educational Resources Information Center

Mantri, Archana

2014-01-01

The intent of the study presented in this paper is to show that the model of problem-based learning (PBL) can be made scalable by designing curriculum around a set of open-ended problems (OEPs). The detailed statistical analysis of the data collected to measure the effects of traditional and PBL instructions for three courses in Electronics and…

The learning curve of robot-assisted laparoscopic fundoplication in children: a prospective evaluation and CUSUM analysis.

PubMed

Cundy, Thomas P; Rowland, Simon P; Gattas, Nicholas E; White, Alan D; Najmaldin, Azad S

2015-06-01

Fundoplication is a leading application of robotic surgery in children, yet the learning curve for this procedure (RF) remains ill-defined. This study aims to identify various learning curve transition points, using cumulative summation (CUSUM) analysis. A prospective database was examined to identify RF cases undertaken during 2006-2014. Time-based surgical process outcomes were evaluated, as well as clinical outcomes. A total of 57 RF cases were included. Statistically significant transitions beyond the learning phase were observed at cases 42, 34 and 37 for docking, console and total operating room times, respectively. A steep early learning phase for docking time was overcome after 12 cases. There were three Clavien-Dindo grade ≥ 3 complications, with two patients requiring redo fundoplication. We identified numerous well-defined learning curve trends to affirm that experience confers significant temporal improvements. Our findings highlight the value of the CUSUM method for learning curve evaluation. Copyright © 2014 John Wiley & Sons, Ltd.

Pattern Activity Clustering and Evaluation (PACE)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Banas, Christopher; Paul, Michael; Bussjager, Becky; Seetharaman, Guna

2012-06-01

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

Experiential Collaborative Learning and Preferential Thinking

NASA Astrophysics Data System (ADS)

Volpentesta, Antonio P.; Ammirato, Salvatore; Sofo, Francesco

The paper presents a Project-Based Learning (shortly, PBL) approach in a collaborative educational environment aimed to develop design ability and creativity of students coming from different engineering disciplines. Three collaborative learning experiences in product design were conducted in order to study their impact on preferred thinking styles of students. Using a thinking style inventory, pre- and post-survey data was collected and successively analyzed through ANOVA techniques. Statistically significant results showed students successfully developed empathy and an openness to multiple perspectives. Furthermore, data analysis confirms that the proposed collaborative learning experience positively contributes to increase awareness in students' thinking styles.

What You Learn is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning

PubMed Central

Smith, Linda

2016-01-01

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps to use eye movement data to infer underlying real-time processes in statistical word learning. PMID:22213894

Learning curve analysis of mitral valve repair using telemanipulative technology.

PubMed

Charland, Patrick J; Robbins, Tom; Rodriguez, Evilio; Nifong, Wiley L; Chitwood, Randolph W

2011-08-01

To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. We found a statistically significant learning curve (P < .01). The institutional learning percentage∗ derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R(2) = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis. Copyright © 2011 The American Association for Thoracic Surgery. All rights reserved.

Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise.

PubMed

Stevens, David J; Arciuli, Joanne; Anderson, David I

2016-05-01

This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions-a control group, a group that exercised for 15 min prior to the statistical learning task, and a group that exercised for 30 min prior to the statistical learning task. The participants in the exercise groups cycled at 60% of their respective V˙O2 max. Each group demonstrated significant statistical learning, with similar levels of learning among the three groups. Contrary to previous research that has shown that a prior bout of exercise can affect performance on explicit cognitive tasks, the results of the current study suggest that the physiological stress induced by moderate-intensity exercise does not affect implicit cognition as measured by statistical learning. Copyright © 2015 Cognitive Science Society, Inc.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Electrophysiological Evidence of Heterogeneity in Visual Statistical Learning in Young Children with ASD

ERIC Educational Resources Information Center

Jeste, Shafali S.; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J.; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F. N.; Johnson, Scott P.

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism…

The Necessity of the Hippocampus for Statistical Learning

PubMed Central

Covington, Natalie V.; Brown-Schmidt, Sarah; Duff, Melissa C.

2018-01-01

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or a broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning. PMID:29308986

Machine learning to analyze images of shocked materials for precise and accurate measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Alternative and traditional assessments: Their comparative impact on students' attitudes and science learning outcomes. An exploratory study

NASA Astrophysics Data System (ADS)

Century, Daisy Nelson

This probing study focused on alternative and traditional assessments, their comparative impacts on students' attitudes and science learning outcomes. Four basic questions were asked: What type of science learning stemming from the instruction can best be assessed by the use of traditional paper-and pencil test? What type of science learning stemming from the instruction can best be assessed by the use of alternative assessment? What are the differences in the types of learning outcomes that can be assessed by the use of paper-pencil test and alternative assessment test? Is there a difference in students' attitude towards learning science when assessment of outcomes is by alternative assessment means compared to traditional means compared to traditional means? A mixed methodology involving quantitative and qualitative techniques was utilized. However, the study was essentially a case study. Quantitative data analysis included content achievement and attitude results, to which non-parametric statistics were applied. Analysis of qualitative data was done as a case study utilizing pre-set protocols resulting in a narrative summary style of report. These outcomes were combined in order to produce conclusions. This study revealed that the traditional method yielded more concrete cognitive content learning than did the alternative assessment. The alternative assessment yielded more psychomotor, cooperative learning and critical thinking skills. In both the alternative and the traditional methods the student's attitudes toward science were positive. There was no significant differences favoring either group. The quantitative findings of no statistically significant differences suggest that at a minimum there is no loss in the use of alternative assessment methods, in this instance, performance testing. Adding the results from the qualitative analysis to this suggests (1) that class groups were more satisfied when alternative methods were employed, and (2) that the two assessment methodologies are complementary to each other, and thus should probably be used together to produce maximum benefit.

Online neural monitoring of statistical learning.

PubMed

Batterink, Laura J; Paller, Ken A

2017-05-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mapping remote and multidisciplinary learning barriers: lessons from challenge-based innovation at CERN

NASA Astrophysics Data System (ADS)

Jensen, Matilde Bisballe; Utriainen, Tuuli Maria; Steinert, Martin

2018-01-01

This paper presents the experienced difficulties of students participating in the multidisciplinary, remote collaborating engineering design course challenge-based innovation at CERN. This is with the aim to identify learning barriers and improve future learning experiences. We statistically analyse the rated differences between distinct design activities, educational background and remote vs. co-located collaboration. The analysis is based on a quantitative and qualitative questionnaire (N = 37). Our analysis found significant ranking differences between remote and co-located activities. This questions whether the remote factor might be a barrier for the originally intended learning goals. Further a correlation between analytical and converging design phases was identified. Hence, future facilitators are suggested to help students in the transition from one design phase to the next rather than only teaching methods in the individual design phases. Finally, we discuss how educators address the identified learning barriers when designing future courses including multidisciplinary or remote collaboration.

Evaluating a Health Educational First aid Program with the Implementation of Synchronous Distance Learning.

PubMed

Ponirou, Paraskevi; Diomidous, Marianna; Mantas, John; Kalokairinou, Athena; Kalouri, Ourania; Kapadochos, Theodoros; Tzavara, Chara

2014-01-01

The education in First Aid through health education programs can help in promoting the health of the population. Meanwhile, the development of alternative forms of education with emphasis on distance learning implemented with e-learning creates an innovative system of knowledge and skills in different population groups. The main purpose of this research proposal is to investigate the effectiveness of the educational program to candidates educators about knowledge and emergency preparedness at school. The study used the Solomon four group design (2 intervention groups and 2 control groups). Statistical analysis showed significant difference within the four groups. Intervention groups had improved significantly their knowledge showing that the program was effective and that they would eventually deal with a threatening situation with right handlings. There were no statistical significant findings regarding other independent variables (p>0,05).The health education program with the implementation of synchronous distance learning succeeded to enhance the knowledge of candidates educators.

Comparing associative, statistical, and inferential reasoning accounts of human contingency learning

PubMed Central

Pineño, Oskar; Miller, Ralph R.

2007-01-01

For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years. PMID:17366303

The results of STEM education methods for enhancing critical thinking and problem solving skill in physics the 10th grade level

NASA Astrophysics Data System (ADS)

Soros, P.; Ponkham, K.; Ekkapim, S.

2018-01-01

This research aimed to: 1) compare the critical think and problem solving skills before and after learning using STEM Education plan, 2) compare student achievement before and after learning about force and laws of motion using STEM Education plan, and 3) the satisfaction of learning by using STEM Education. The sample used were 37 students from grade 10 at Borabu School, Borabu District, Mahasarakham Province, semester 2, Academic year 2016. Tools used in this study consist of: 1) STEM Education plan about the force and laws of motion for grade 10 students of 1 schemes with total of 14 hours, 2) The test of critical think and problem solving skills with multiple-choice type of 5 options and 2 option of 30 items, 3) achievement test on force and laws of motion with multiple-choice of 4 options of 30 items, 4) satisfaction learning with 5 Rating Scale of 20 items. The statistics used in data analysis were percentage, mean, standard deviation, and t-test (Dependent). The results showed that 1) The student with learning using STEM Education plan have score of critical think and problem solving skills on post-test higher than pre-test with statistically significant level .01. 2) The student with learning using STEM Education plan have achievement score on post-test higher than pre-test with statistically significant level of .01. 3) The student'level of satisfaction toward the learning by using STEM Education plan was at a high level (X ¯ = 4.51, S.D=0.56).

The effectiveness of problem-based learning on development of nursing students' critical thinking: a systematic review and meta-analysis.

PubMed

Kong, Ling-Na; Qin, Bo; Zhou, Ying-qing; Mou, Shao-yu; Gao, Hui-Ming

2014-03-01

The objective of this systematic review and meta-analysis was to estimate the effectiveness of problem-based learning in developing nursing students' critical thinking. Searches of PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Proquest, Cochrane Central Register of Controlled Trials (CENTRAL) and China National Knowledge Infrastructure (CNKI) were undertaken to identify randomized controlled trails from 1965 to December 2012, comparing problem-based learning with traditional lectures on the effectiveness of development of nursing students' critical thinking, with no language limitation. The mesh-terms or key words used in the search were problem-based learning, thinking, critical thinking, nursing, nursing education, nurse education, nurse students, nursing students and pupil nurse. Two reviewers independently assessed eligibility and extracted data. Quality assessment was conducted independently by two reviewers using the Cochrane Collaboration's Risk of Bias Tool. We analyzed critical thinking scores (continuous outcomes) using a standardized mean difference (SMD) or weighted mean difference (WMD) with a 95% confidence intervals (CIs). Heterogeneity was assessed using the Cochran's Q statistic and I(2) statistic. Publication bias was assessed by means of funnel plot and Egger's test of asymmetry. Nine articles representing eight randomized controlled trials were included in the meta-analysis. Most studies were of low risk of bias. The pooled effect size showed problem-based learning was able to improve nursing students' critical thinking (overall critical thinking scores SMD=0.33, 95%CI=0.13-0.52, P=0.0009), compared with traditional lectures. There was low heterogeneity (overall critical thinking scores I(2)=45%, P=0.07) in the meta-analysis. No significant publication bias was observed regarding overall critical thinking scores (P=0.536). Sensitivity analysis showed that the result of our meta-analysis was reliable. Most effect sizes for subscales of the California Critical Thinking Dispositions Inventory (CCTDI) and Bloom's Taxonomy favored problem-based learning, while effect sizes for all subscales of the California Critical Thinking Skills Test (CCTST) and most subscales of the Watson-Glaser Critical Thinking Appraisal (WCGTA) were inconclusive. The results of the current meta-analysis indicate that problem-based learning might help nursing students to improve their critical thinking. More research with larger sample size and high quality in different nursing educational contexts are required. Copyright © 2013 Elsevier Ltd. All rights reserved.

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

PubMed

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Learning from Commercials: The Influence of TV Advertising on the Voter Political "Agenda."

ERIC Educational Resources Information Center

Shaw, Donald L.; Bowers, Thomas A.

The effects of the television advertisements for Richard Nixon and George McGovern during the 1972 presidential election were tested by a content analysis of television programing and statistical analysis of viewer attitudinal response. Programing content for Nixon developed more general issues and did not especially feature the personality of…

Teaching for Art Criticism: Incorporating Feldman's Critical Analysis Learning Model in Students' Studio Practice

ERIC Educational Resources Information Center

Subramaniam, Maithreyi; Hanafi, Jaffri; Putih, Abu Talib

2016-01-01

This study adopted 30 first year graphic design students' artwork, with critical analysis using Feldman's model of art criticism. Data were analyzed quantitatively; descriptive statistical techniques were employed. The scores were viewed in the form of mean score and frequencies to determine students' performances in their critical ability.…

Technologies for Teaching and Learning about Box Plots and Statistical Analysis

ERIC Educational Resources Information Center

Forster, Patricia A.

2007-01-01

This paper analyses technology-based instruction on data-analysis with box plots. Examples of instruction taken from the research literature inform a study of two classes of 17 year-old students (upper secondary) in which the mathematical relationships that their teachers targeted are distinguished as being, or not being, relevant to statistical…

Computers and Student Learning: Interpreting the Multivariate Analysis of PISA 2000

ERIC Educational Resources Information Center

Bielefeldt, Talbot

2005-01-01

In November 2004, economists Thomas Fuchs and Ludger Woessmann published a statistical analysis of the relationship between technology and student achievement using year 2000 data from the Programme for International Student Assessment (PISA). The 2000 PISA was the first in a series of triennial assessments of 15-year-olds conducted by the…

Investigating Students' Acceptance of a Statistics Learning Platform Using Technology Acceptance Model

ERIC Educational Resources Information Center

Song, Yanjie; Kong, Siu-Cheung

2017-01-01

The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a…

Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

ERIC Educational Resources Information Center

Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.

2010-01-01

Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…

The Impact of Language Experience on Language and Reading: A Statistical Learning Approach

ERIC Educational Resources Information Center

Seidenberg, Mark S.; MacDonald, Maryellen C.

2018-01-01

This article reviews the important role of statistical learning for language and reading development. Although statistical learning--the unconscious encoding of patterns in language input--has become widely known as a force in infants' early interpretation of speech, the role of this kind of learning for language and reading comprehension in…

Reducing statistics anxiety and enhancing statistics learning achievement: effectiveness of a one-minute strategy.

PubMed

Chiou, Chei-Chang; Wang, Yu-Min; Lee, Li-Tze

2014-08-01

Statistical knowledge is widely used in academia; however, statistics teachers struggle with the issue of how to reduce students' statistics anxiety and enhance students' statistics learning. This study assesses the effectiveness of a "one-minute paper strategy" in reducing students' statistics-related anxiety and in improving students' statistics-related achievement. Participants were 77 undergraduates from two classes enrolled in applied statistics courses. An experiment was implemented according to a pretest/posttest comparison group design. The quasi-experimental design showed that the one-minute paper strategy significantly reduced students' statistics anxiety and improved students' statistics learning achievement. The strategy was a better instructional tool than the textbook exercise for reducing students' statistics anxiety and improving students' statistics achievement.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Finnish upper secondary students' collaborative processes in learning statistics in a CSCL environment

NASA Astrophysics Data System (ADS)

Kaleva Oikarinen, Juho; Järvelä, Sanna; Kaasila, Raimo

2014-04-01

This design-based research project focuses on documenting statistical learning among 16-17-year-old Finnish upper secondary school students (N = 78) in a computer-supported collaborative learning (CSCL) environment. One novel value of this study is in reporting the shift from teacher-led mathematical teaching to autonomous small-group learning in statistics. The main aim of this study is to examine how student collaboration occurs in learning statistics in a CSCL environment. The data include material from videotaped classroom observations and the researcher's notes. In this paper, the inter-subjective phenomena of students' interactions in a CSCL environment are analysed by using a contact summary sheet (CSS). The development of the multi-dimensional coding procedure of the CSS instrument is presented. Aptly selected video episodes were transcribed and coded in terms of conversational acts, which were divided into non-task-related and task-related categories to depict students' levels of collaboration. The results show that collaborative learning (CL) can facilitate cohesion and responsibility and reduce students' feelings of detachment in our classless, periodic school system. The interactive .pdf material and collaboration in small groups enable statistical learning. It is concluded that CSCL is one possible method of promoting statistical teaching. CL using interactive materials seems to foster and facilitate statistical learning processes.

Assessing segmentation processes by click detection: online measure of statistical learning, or simple interference?

PubMed

Franco, Ana; Gaillard, Vinciane; Cleeremans, Axel; Destrebecqz, Arnaud

2015-12-01

Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212-223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

The Learning Preferences of Applicants Who Interview for General Surgery Residency: A Multiinstitutional Study.

PubMed

Kim, Roger H; Kurtzman, Scott H; Collier, Ashley N; Shabahang, Mohsen M

Learning styles theory posits that learners have distinct preferences for how they assimilate new information. The VARK model categorizes learners based on combinations of 4 learning preferences: visual (V), aural (A), read/write (R), and kinesthetic (K). A previous single institution study demonstrated that the VARK preferences of applicants who interview for general surgery residency are different from that of the general population and that learning preferences were associated with performance on standardized tests. This multiinstitutional study was conducted to determine the distribution of VARK preferences among interviewees for general surgery residency and the effect of those preferences on United States Medical Licensing Examination (USMLE) scores. The VARK learning inventory was administered to applicants who interviewed at 3 general surgery programs during the 2014 to 2015 academic year. The distribution of VARK learning preferences among interviewees was compared with that of the general population of VARK respondents. Performance on USMLE Step 1 and Step 2 Clinical Knowledge was analyzed for associations with VARK learning preferences. Chi-square, analysis of variance, and Dunnett's test were used for statistical analysis, with p < 0.05 considered statistically significant. The VARK inventory was completed by a total of 140 residency interviewees. Sixty-four percent of participants were male, and 41% were unimodal, having a preference for a single learning modality. The distribution of VARK preferences of interviewees was different than that of the general population (p = 0.02). By analysis of variance, there were no overall differences in USMLE Step 1 and Step 2 Clinical Knowledge scores by VARK preference (p = 0.06 and 0.21, respectively). However, multiple comparison analysis using Dunnett's test revealed that interviewees with R preferences had significantly higher scores than those with multimodal preferences on USMLE Step 1 (239 vs. 222, p = 0.02). Applicants who interview for general surgery residency have a different pattern of VARK preferences than that of the general population. Interviewees with preferences for read/write learning modalities have higher scores on the USMLE Step 1 than those with multimodal preferences. Learning preferences may have impact on residency applicant selection and represents a topic that warrants further investigation. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Statistical learning using real-world scenes: extracting categorical regularities without conscious intent.

PubMed

Brady, Timothy F; Oliva, Aude

2008-07-01

Recent work has shown that observers can parse streams of syllables, tones, or visual shapes and learn statistical regularities in them without conscious intent (e.g., learn that A is always followed by B). Here, we demonstrate that these statistical-learning mechanisms can operate at an abstract, conceptual level. In Experiments 1 and 2, observers incidentally learned which semantic categories of natural scenes covaried (e.g., kitchen scenes were always followed by forest scenes). In Experiments 3 and 4, category learning with images of scenes transferred to words that represented the categories. In each experiment, the category of the scenes was irrelevant to the task. Together, these results suggest that statistical-learning mechanisms can operate at a categorical level, enabling generalization of learned regularities using existing conceptual knowledge. Such mechanisms may guide learning in domains as disparate as the acquisition of causal knowledge and the development of cognitive maps from environmental exploration.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students.

PubMed

Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah

2015-01-01

Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Examination of the teaching styles of nursing professional development specialists, part II: correlational study on teaching styles and use of adult learning theory.

PubMed

Curran, Mary K

2014-08-01

This article, the second in a two-part series, details a correlational study that examined the effects of four variables (graduate degrees in nursing education, professional development training in adult learning theory, nursing professional development [NPD] certification, and NPD specialist experience) on the use of adult learning theory to guide curriculum development. Using the Principles of Adult Learning Scale, 114 NPD specialists tested the hypothesis that NPD specialists with graduate degrees in nursing education, professional development training in adult learning theory, NPD certification, and NPD experience would use higher levels of adult learning theory in their teaching practices to guide curriculum development than those without these attributes. This hypothesis was rejected as regression analysis revealed only one statistically significant predictor variable, NPD certification, influenced the use of adult learning theory. In addition, analysis revealed NPD specialists tended to support a teacher-centered rather than a learner-centered teaching style, indicating NPD educators are not using adult learning theory to guide teaching practices and curriculum development.

An exploratory study of the relationship between learning styles and academic performance among students in different nursing programs.

PubMed

Li, Yuh-Shiow; Yu, Wen-Pin; Liu, Chin-Fang; Shieh, Sue-Heui; Yang, Bao-Huan

2014-10-27

Abstract Background: Learning style is a major consideration in planning for effective and efficient instruction and learning. Learning style has been shown to influence academic performance in the previous research. Little is known about Taiwanese students' learning styles, particularly in the field of nursing education. Aim: This purpose of this study was to identify the relationship between learning styles and academic performance among nursing students in a five-year associate degree of nursing (ADN) program and a two-year bachelor of science in nursing (BSN) program in Taiwan. Methods/Design: This study employed a descriptive and exploratory design. The Chinese version of the Myers-Briggs Type Indicator (MBTI) Form M was an instrument. Data such as grade point average (GPA) were obtained from the Office of Academic Affairs and the Registrar computerized records. Descriptive statistics, one-way analysis of variance ANOVA) and chi-square statistical analysis were used to explore the relationship between academic performance and learning style in Taiwanese nursing students. Results/Findings: The study sample included 285 nursing students: 96 students in a two-year BSN program, and 189 students in a five-year ADN program. Two common learning styles were found: introversion, sensing, thinking, and judging (ISTJ); and introversion, sensing, feeling, and judging (ISFJ). A sensing-judging pair was identified in 43.3% of the participants. Academic performance was significantly related to learning style (p < 0.05, d.f. = 15). Conclusion: The results of this study can help educators devise classroom and clinical instructional strategies that respond to individual needs in order to maximize academic performance and enhance student success. A large sample is recommended for further research. Understanding the learning style preferences of students can enhance learning for those who are under performing in their academic studies, thereby enhancing nursing education.

Statistically optimal perception and learning: from behavior to neural representations

PubMed Central

Fiser, József; Berkes, Pietro; Orbán, Gergő; Lengyel, Máté

2010-01-01

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty. PMID:20153683

Evaluating Computer-Based Simulations, Multimedia and Animations that Help Integrate Blended Learning with Lectures in First Year Statistics

ERIC Educational Resources Information Center

Neumann, David L.; Neumann, Michelle M.; Hood, Michelle

2011-01-01

The discipline of statistics seems well suited to the integration of technology in a lecture as a means to enhance student learning and engagement. Technology can be used to simulate statistical concepts, create interactive learning exercises, and illustrate real world applications of statistics. The present study aimed to better understand the…

Attitudes of Medical Graduate and Undergraduate Students toward the Learning and Application of Medical Statistics

ERIC Educational Resources Information Center

Wu, Yazhou; Zhang, Ling; Liu, Ling; Zhang, Yanqi; Liu, Xiaoyu; Yi, Dong

2015-01-01

It is clear that the teaching of medical statistics needs to be improved, yet areas for priority are unclear as medical students' learning and application of statistics at different levels is not well known. Our goal is to assess the attitudes of medical students toward the learning and application of medical statistics, and discover their…

Developing Conceptual Understanding in a Statistics Course: Merrill's First Principles and Real Data at Work

ERIC Educational Resources Information Center

Tu, Wendy; Snyder, Martha M.

2017-01-01

Difficulties in learning statistics primarily at the college-level led to a reform movement in statistics education in the early 1990s. Although much work has been done, effective learning designs that facilitate active learning, conceptual understanding of statistics, and the use of real-data in the classroom are needed. Guided by Merrill's First…

Preserved Statistical Learning of Tonal and Linguistic Material in Congenital Amusia

PubMed Central

Omigie, Diana; Stewart, Lauren

2011-01-01

Congenital amusia is a lifelong disorder whereby individuals have pervasive difficulties in perceiving and producing music. In contrast, typical individuals display a sophisticated understanding of musical structure, even in the absence of musical training. Previous research has shown that they acquire this knowledge implicitly, through exposure to music's statistical regularities. The present study tested the hypothesis that congenital amusia may result from a failure to internalize statistical regularities – specifically, lower-order transitional probabilities. To explore the specificity of any potential deficits to the musical domain, learning was examined with both tonal and linguistic material. Participants were exposed to structured tonal and linguistic sequences and, in a subsequent test phase, were required to identify items which had been heard in the exposure phase, as distinct from foils comprising elements that had been present during exposure, but presented in a different temporal order. Amusic and control individuals showed comparable learning, for both tonal and linguistic material, even when the tonal stream included pitch intervals around one semitone. However analysis of binary confidence ratings revealed that amusic individuals have less confidence in their abilities and that their performance in learning tasks may not be contingent on explicit knowledge formation or level of awareness to the degree shown in typical individuals. The current findings suggest that the difficulties amusic individuals have with real-world music cannot be accounted for by an inability to internalize lower-order statistical regularities but may arise from other factors. PMID:21779263

Learning Scene Categories from High Resolution Satellite Image for Aerial Video Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheriyadat, Anil M

2011-01-01

Automatic scene categorization can benefit various aerial video processing applications. This paper addresses the problem of predicting the scene category from aerial video frames using a prior model learned from satellite imagery. We show that local and global features in the form of line statistics and 2-D power spectrum parameters respectively can characterize the aerial scene well. The line feature statistics and spatial frequency parameters are useful cues to distinguish between different urban scene categories. We learn the scene prediction model from highresolution satellite imagery to test the model on the Columbus Surrogate Unmanned Aerial Vehicle (CSUAV) dataset ollected bymore » high-altitude wide area UAV sensor platform. e compare the proposed features with the popular Scale nvariant Feature Transform (SIFT) features. Our experimental results show that proposed approach outperforms te SIFT model when the training and testing are conducted n disparate data sources.« less

Physics Teachers and Students: A Statistical and Historical Analysis of Women

NASA Astrophysics Data System (ADS)

Gregory, Amanda

2009-10-01

Historically, women have been denied an education comparable to that available to men. Since women have been allowed into institutions of higher learning, they have been studying and earning physics degrees. The aim of this poster is to discuss the statistical relationship between the number of women enrolled in university physics programs and the number of female physics faculty members. Special care has been given to examining the statistical data in the context of the social climate at the time that these women were teaching or pursuing their education.

Statistical Learning and Language: An Individual Differences Study

ERIC Educational Resources Information Center

Misyak, Jennifer B.; Christiansen, Morten H.

2012-01-01

Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered…

Statistical Learning of Probabilistic Nonadjacent Dependencies by Multiple-Cue Integration

ERIC Educational Resources Information Center

van den Bos, Esther; Christiansen, Morten H.; Misyak, Jennifer B.

2012-01-01

Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements…

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593

Utah Virtual Lab: JAVA interactivity for teaching science and statistics on line.

PubMed

Malloy, T E; Jensen, G C

2001-05-01

The Utah on-line Virtual Lab is a JAVA program run dynamically off a database. It is embedded in StatCenter (www.psych.utah.edu/learn/statsampler.html), an on-line collection of tools and text for teaching and learning statistics. Instructors author a statistical virtual reality that simulates theories and data in a specific research focus area by defining independent, predictor, and dependent variables and the relations among them. Students work in an on-line virtual environment to discover the principles of this simulated reality: They go to a library, read theoretical overviews and scientific puzzles, and then go to a lab, design a study, collect and analyze data, and write a report. Each student's design and data analysis decisions are computer-graded and recorded in a database; the written research report can be read by the instructor or by other students in peer groups simulating scientific conventions.

Grounding statistical learning in context: The effects of learning and retrieval contexts on cross-situational word learning.

PubMed

Chen, Chi-Hsin; Yu, Chen

2017-06-01

Natural language environments usually provide structured contexts for learning. This study examined the effects of semantically themed contexts-in both learning and retrieval phases-on statistical word learning. Results from 2 experiments consistently showed that participants had higher performance in semantically themed learning contexts. In contrast, themed retrieval contexts did not affect performance. Our work suggests that word learners are sensitive to statistical regularities not just at the level of individual word-object co-occurrences but also at another level containing a whole network of associations among objects and their properties.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Statistical learning and auditory processing in children with music training: An ERP study.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Ibrahim, Ronny; Arciuli, Joanne

2017-07-01

The question whether musical training is associated with enhanced auditory and cognitive abilities in children is of considerable interest. In the present study, we compared children with music training versus those without music training across a range of auditory and cognitive measures, including the ability to detect implicitly statistical regularities in input (statistical learning). Statistical learning of regularities embedded in auditory and visual stimuli was measured in musically trained and age-matched untrained children between the ages of 9-11years. In addition to collecting behavioural measures, we recorded electrophysiological measures to obtain an online measure of segmentation during the statistical learning tasks. Musically trained children showed better performance on melody discrimination, rhythm discrimination, frequency discrimination, and auditory statistical learning. Furthermore, grand-averaged ERPs showed that triplet onset (initial stimulus) elicited larger responses in the musically trained children during both auditory and visual statistical learning tasks. In addition, children's music skills were associated with performance on auditory and visual behavioural statistical learning tasks. Our data suggests that individual differences in musical skills are associated with children's ability to detect regularities. The ERP data suggest that musical training is associated with better encoding of both auditory and visual stimuli. Although causality must be explored in further research, these results may have implications for developing music-based remediation strategies for children with learning impairments. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Using statistical text classification to identify health information technology incidents

PubMed Central

Chai, Kevin E K; Anthony, Stephen; Coiera, Enrico; Magrabi, Farah

2013-01-01

Objective To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. Design We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both ‘balanced’ (50% HIT) and ‘stratified’ (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined. Measurements κ statistic, F1 score, precision and recall. Results Classification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165). Conclusions Statistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation. PMID:23666777

Application of the statistical process control method for prospective patient safety monitoring during the learning phase: robotic kidney transplantation with regional hypothermia (IDEAL phase 2a-b).

PubMed

Sood, Akshay; Ghani, Khurshid R; Ahlawat, Rajesh; Modi, Pranjal; Abaza, Ronney; Jeong, Wooju; Sammon, Jesse D; Diaz, Mireya; Kher, Vijay; Menon, Mani; Bhandari, Mahendra

2014-08-01

Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption phase. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Crux: Rapid Open Source Protein Tandem Mass Spectrometry Analysis

PubMed Central

2015-01-01

Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit (http://cruxtoolkit.sourceforge.net) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data. PMID:25182276

Concurrent Movement Impairs Incidental but Not Intentional Statistical Learning

ERIC Educational Resources Information Center

Stevens, David J.; Arciuli, Joanne; Anderson, David I.

2015-01-01

The effect of concurrent movement on incidental versus intentional statistical learning was examined in two experiments. In Experiment 1, participants learned the statistical regularities embedded within familiarization stimuli implicitly, whereas in Experiment 2 they were made aware of the embedded regularities and were instructed explicitly to…

Explorations in Statistics: Correlation

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This sixth installment of "Explorations in Statistics" explores correlation, a familiar technique that estimates the magnitude of a straight-line relationship between two variables. Correlation is meaningful only when the…

Algorithmic detectability threshold of the stochastic block model

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Learning of Grammar-Like Visual Sequences by Adults with and without Language-Learning Disabilities

ERIC Educational Resources Information Center

Aguilar, Jessica M.; Plante, Elena

2014-01-01

Purpose: Two studies examined learning of grammar-like visual sequences to determine whether a general deficit in statistical learning characterizes this population. Furthermore, we tested the hypothesis that difficulty in sustaining attention during the learning task might account for differences in statistical learning. Method: In Study 1,…

Learning Styles Preferences of Statistics Students: A Study in the Faculty of Business and Economics at the UAE University

ERIC Educational Resources Information Center

Yousef, Darwish Abdulrahman

2016-01-01

Purpose: Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates…

Statistics Anxiety, Trait Anxiety, Learning Behavior, and Academic Performance

ERIC Educational Resources Information Center

Macher, Daniel; Paechter, Manuela; Papousek, Ilona; Ruggeri, Kai

2012-01-01

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N = 147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical…

Statistical Learning in a Natural Language by 8-Month-Old Infants

PubMed Central

Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

2013-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896

Statistical learning in a natural language by 8-month-old infants.

PubMed

Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R

2009-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.

Musical Experience Influences Statistical Learning of a Novel Language

PubMed Central

Shook, Anthony; Marian, Viorica; Bartolotti, James; Schroeder, Scott R.

2014-01-01

Musical experience may benefit learning a new language by enhancing the fidelity with which the auditory system encodes sound. In the current study, participants with varying degrees of musical experience were exposed to two statistically-defined languages consisting of auditory Morse-code sequences which varied in difficulty. We found an advantage for highly-skilled musicians, relative to less-skilled musicians, in learning novel Morse-code based words. Furthermore, in the more difficult learning condition, performance of lower-skilled musicians was mediated by their general cognitive abilities. We suggest that musical experience may lead to enhanced processing of statistical information and that musicians’ enhanced ability to learn statistical probabilities in a novel Morse-code language may extend to natural language learning. PMID:23505962

Analysis of Student Activity in Web-Supported Courses as a Tool for Predicting Dropout

ERIC Educational Resources Information Center

Cohen, Anat

2017-01-01

Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student…

The Role of the Company in Generating Skills. The Learning Effects of Work Organization. Denmark.

ERIC Educational Resources Information Center

Kristensen, Peer Hull; Petersen, James Hopner

The impact of developments in work organizations on the skilling process in Denmark was studied through a macro analysis of available statistical information about the development of workplace training in Denmark and case studies of three Danish firms. The macro analysis focused on the following: Denmark's vocational training system; the Danish…

A Meta-Analysis of the Effects of Computer Technology on School Students' Mathematics Learning

ERIC Educational Resources Information Center

Li, Qing; Ma, Xin

2010-01-01

This study examines the impact of computer technology (CT) on mathematics education in K-12 classrooms through a systematic review of existing literature. A meta-analysis of 85 independent effect sizes extracted from 46 primary studies involving a total of 36,793 learners indicated statistically significant positive effects of CT on mathematics…

How to Use Value-Added Analysis to Improve Student Learning: A Field Guide for School and District Leaders

ERIC Educational Resources Information Center

Kennedy, Kate; Peters, Mary; Thomas, Mike

2012-01-01

Value-added analysis is the most robust, statistically significant method available for helping educators quantify student progress over time. This powerful tool also reveals tangible strategies for improving instruction. Built around the work of Battelle for Kids, this book provides a field-tested continuous improvement model for using…

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

PubMed

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

PubMed Central

Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated. PMID:28886019

Explorations in Statistics: Power

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fifth installment of "Explorations in Statistics" revisits power, a concept fundamental to the test of a null hypothesis. Power is the probability that we reject the null hypothesis when it is false. Four…

Explorations in Statistics: Confidence Intervals

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This third installment of "Explorations in Statistics" investigates confidence intervals. A confidence interval is a range that we expect, with some level of confidence, to include the true value of a population parameter…

Explorations in Statistics: Permutation Methods

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2012-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eighth installment of "Explorations in Statistics" explores permutation methods, empiric procedures we can use to assess an experimental result--to test a null hypothesis--when we are reluctant to trust statistical…

Statistical Learning of Phonetic Categories: Insights from a Computational Approach

ERIC Educational Resources Information Center

McMurray, Bob; Aslin, Richard N.; Toscano, Joseph C.

2009-01-01

Recent evidence (Maye, Werker & Gerken, 2002) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model…

Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study

PubMed Central

Teinonen, Tuomas; Tervaniemi, Mari; Huotilainen, Minna

2016-01-01

Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0–100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200–400 ms and 450–650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns. PMID:27617967

Factors associated with student learning processes in primary health care units: a questionnaire study.

PubMed

Bos, Elisabeth; Alinaghizadeh, Hassan; Saarikoski, Mikko; Kaila, Päivi

2015-01-01

Clinical placement plays a key role in education intended to develop nursing and caregiving skills. Studies of nursing students' clinical learning experiences show that these dimensions affect learning processes: (i) supervisory relationship, (ii) pedagogical atmosphere, (iii) management leadership style, (iv) premises of nursing care on the ward, and (v) nursing teachers' roles. Few empirical studies address the probability of an association between these dimensions and factors such as student (a) motivation, (b) satisfaction with clinical placement, and (c) experiences with professional role models. The study aimed to investigate factors associated with the five dimensions in clinical learning environments within primary health care units. The Swedish version of Clinical Learning Environment, Supervision and Teacher, a validated evaluation scale, was administered to 356 graduating nursing students after four or five weeks clinical placement in primary health care units. Response rate was 84%. Multivariate analysis of variance is determined if the five dimensions are associated with factors a, b, and c above. The analysis revealed a statistically significant association with the five dimensions and two factors: students' motivation and experiences with professional role models. The satisfaction factor had a statistically significant association (effect size was high) with all dimensions; this clearly indicates that students experienced satisfaction. These questionnaire results show that a good clinical learning experience constitutes a complex whole (totality) that involves several interacting factors. Supervisory relationship and pedagogical atmosphere particularly influenced students' satisfaction and motivation. These results provide valuable decision-support material for clinical education planning, implementation, and management. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Applicability of Standard Error of Measurement and Minimal Detectable Change to Motor Learning Research-A Behavioral Study.

PubMed

Furlan, Leonardo; Sterr, Annette

2018-01-01

Motor learning studies face the challenge of differentiating between real changes in performance and random measurement error. While the traditional p -value-based analyses of difference (e.g., t -tests, ANOVAs) provide information on the statistical significance of a reported change in performance scores, they do not inform as to the likely cause or origin of that change, that is, the contribution of both real modifications in performance and random measurement error to the reported change. One way of differentiating between real change and random measurement error is through the utilization of the statistics of standard error of measurement (SEM) and minimal detectable change (MDC). SEM is estimated from the standard deviation of a sample of scores at baseline and a test-retest reliability index of the measurement instrument or test employed. MDC, in turn, is estimated from SEM and a degree of confidence, usually 95%. The MDC value might be regarded as the minimum amount of change that needs to be observed for it to be considered a real change, or a change to which the contribution of real modifications in performance is likely to be greater than that of random measurement error. A computer-based motor task was designed to illustrate the applicability of SEM and MDC to motor learning research. Two studies were conducted with healthy participants. Study 1 assessed the test-retest reliability of the task and Study 2 consisted in a typical motor learning study, where participants practiced the task for five consecutive days. In Study 2, the data were analyzed with a traditional p -value-based analysis of difference (ANOVA) and also with SEM and MDC. The findings showed good test-retest reliability for the task and that the p -value-based analysis alone identified statistically significant improvements in performance over time even when the observed changes could in fact have been smaller than the MDC and thereby caused mostly by random measurement error, as opposed to by learning. We suggest therefore that motor learning studies could complement their p -value-based analyses of difference with statistics such as SEM and MDC in order to inform as to the likely cause or origin of any reported changes in performance.

Healthcare students' experiences when integrating e-learning and flipped classroom instructional approaches.

PubMed

Telford, Mark; Senior, Emma

2017-06-08

This article describes the experiences of undergraduate healthcare students taking a module adopting a 'flipped classroom' approach. Evidence suggests that flipped classroom as a pedagogical tool has the potential to enhance student learning and to improve healthcare practice. This innovative approach was implemented within a healthcare curriculum and in a module looking at public health delivered at the beginning of year two of a 3-year programme. The focus of the evaluation study was on the e-learning resources used in the module and the student experiences of these; with a specific aim to evaluate this element of the flipped classroom approach. A mixed-methods approach was adopted and data collected using questionnaires, which were distributed across a whole cohort, and a focus group involving ten participants. Statistical analysis of the data showed the positive student experience of engaging with e-learning. The thematic analysis identified two key themes; factors influencing a positive learning experience and the challenges when developing e-learning within a flipped classroom approach. The study provides guidance for further developments and improvements when developing e-learning as part of the flipped classroom approach.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students

PubMed Central

Saadati, Farzaneh; Ahmad Tarmizi, Rohani

2015-01-01

Because students’ ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is ‘value added’ because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students’ problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students. PMID:26132553

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.

PubMed

Mareschal, Denis; French, Robert M

2017-01-05

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning

PubMed Central

French, Robert M.

2017-01-01

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872375

Students' perspectives of undergraduate research methods education at three public medical schools in Uganda.

PubMed

Munabi, Ian Guyton; Buwembo, William; Joseph, Ruberwa; Peter, Kawungezi; Bajunirwe, Francis; Mwaka, Erisa Sabakaki

2016-01-01

In this study we used a model of adult learning to explore undergraduate students' views on how to improve the teaching of research methods and biostatistics. This was a secondary analysis of survey data of 600 undergraduate students from three medical schools in Uganda. The analysis looked at student's responses to an open ended section of a questionnaire on their views on undergraduate teaching of research methods and biostatistics. Qualitative phenomenological data analysis was done with a bias towards principles of adult learning. Students appreciated the importance of learning research methods and biostatistics as a way of understanding research problems; appropriately interpreting statistical concepts during their training and post-qualification practice; and translating the knowledge acquired. Stressful teaching environment and inadequate educational resource materials were identified as impediments to effective learning. Suggestions for improved learning included: early and continuous exposure to the course; more active and practical approach to teaching; and a need for mentorship. The current methods of teaching research methods and biostatistics leave most of the students in the dissonance phase of learning resulting in none or poor student engagement that results in a failure to comprehend and/or appreciate the principles governing the use of different research methods.

The didactic situation in geometry learning based on analysis of learning obstacles and learning trajectory

NASA Astrophysics Data System (ADS)

Sulistyowati, Fitria; Budiyono, Slamet, Isnandar

2017-12-01

This study aims to design a didactic situation based on the analysis of learning obstacles and learning trajectory on prism volume. The type of this research is qualitative and quantitative research with steps: analyzing the learning obstacles and learning trajectory, preparing the didactic situation, applying the didactic situation in the classroom, mean difference test of problem solving ability with t-test statistic. The subjects of the study were 8th grade junior high school students in Magelang 2016/2017 selected randomly from eight existing classes. The result of this research is the design of didactic situations that can be implemented in prism volume learning. The effectiveness of didactic situations that have been designed is shown by the mean difference test that is the problem solving ability of the students after the application of the didactic situation better than before the application. The didactic situation that has been generated is expected to be a consideration for teachers to design lessons that match the character of learners, classrooms and teachers themselves, so that the potential thinking of learners can be optimized to avoid the accumulation of learning obstacles.

Gender differences in learning physical science concepts: Does computer animation help equalize them?

NASA Astrophysics Data System (ADS)

Jacek, Laura Lee

This dissertation details an experiment designed to identify gender differences in learning using three experimental treatments: animation, static graphics, and verbal instruction alone. Three learning presentations were used in testing of 332 university students. Statistical analysis was performed using ANOVA, binomial tests for differences of proportion, and descriptive statistics. Results showed that animation significantly improved women's long-term learning over static graphics (p = 0.067), but didn't significantly improve men's long-term learning over static graphics. In all cases, women's scores improved with animation over both other forms of instruction for long-term testing, indicating that future research should not abandon the study of animation as a tool that may promote gender equity in science. Short-term test differences were smaller, and not statistically significant. Variation present in short-term scores was related more to presentation topic than treatment. This research also details characteristics of each of the three presentations, to identify variables (e.g. level of abstraction in presentation) affecting score differences within treatments. Differences between men's and women's scores were non-standard between presentations, but these differences were not statistically significant (long-term p = 0.2961, short-term p = 0.2893). In future research, experiments might be better designed to test these presentational variables in isolation, possibly yielding more distinctive differences between presentational scores. Differences in confidence interval overlaps between presentations suggested that treatment superiority may be somewhat dependent on the design or topic of the learning presentation. Confidence intervals greatly overlap in all situations. This undercut, to some degree, the surety of conclusions indicating superiority of one treatment type over the others. However, confidence intervals for animation were smaller, overlapped nearly completely for men and women (there was less overlap between the genders for the other two treatments), and centered around slightly higher means, lending further support to the conclusion that animation helped equalize men's and women's learning. The most important conclusion identified in this research is that gender is an important variable experimental populations testing animation as a learning device. Averages indicated that both men and women prefer to work with animation over either static graphics or verbal instruction alone.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Vocational students' learning preferences: the interpretability of ipsative data.

PubMed

Smith, P J

2000-02-01

A number of researchers have argued that ipsative data are not suitable for statistical procedures designed for normative data. Others have argued that the interpretability of such analyses of ipsative data are little affected where the number of variables and the sample size are sufficiently large. The research reported here represents a factor analysis of the scores on the Canfield Learning Styles Inventory for 1,252 students in vocational education. The results of the factor analysis of these ipsative data were examined in a context of existing theory and research on vocational students and lend support to the argument that the factor analysis of ipsative data can provide sensibly interpretable results.

Diagnosing alternative conceptions of Fermi energy among undergraduate students

NASA Astrophysics Data System (ADS)

Sharma, Sapna; Ahluwalia, Pardeep Kumar

2012-07-01

Physics education researchers have scientifically established the fact that the understanding of new concepts and interpretation of incoming information are strongly influenced by the preexisting knowledge and beliefs of students, called epistemological beliefs. This can lead to a gap between what students actually learn and what the teacher expects them to learn. In a classroom, as a teacher, it is desirable that one tries to bridge this gap at least on the key concepts of a particular field which is being taught. One such key concept which crops up in statistical physics/solid-state physics courses, and around which the behaviour of materials is described, is Fermi energy (εF). In this paper, we present the results which emerged about misconceptions on Fermi energy in the process of administering a diagnostic tool called the Statistical Physics Concept Survey developed by the authors. It deals with eight themes of basic importance in learning undergraduate solid-state physics and statistical physics. The question items of the tool were put through well-established sequential processes: definition of themes, Delphi study, interview with students, drafting questions, administration, validity and reliability of the tool. The tool was administered to a group of undergraduate students and postgraduate students, in a pre-test and post-test design. In this paper, we have taken one of the themes i.e. Fermi energy of the diagnostic tool for our analysis and discussion. Students’ responses and reasoning comments given during interview were analysed. This analysis helped us to identify prevailing misconceptions/learning gaps among students on this topic. How spreadsheets can be effectively used to remove the identified misconceptions and help appreciate the finer nuances while visualizing the behaviour of the system around Fermi energy, normally sidestepped both by the teachers and learners, is also presented in this paper.

Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography.

PubMed

González, Germán; Ash, Samuel Y; Vegas-Sánchez-Ferrero, Gonzalo; Onieva Onieva, Jorge; Rahaghi, Farbod N; Ross, James C; Díaz, Alejandro; San José Estépar, Raúl; Washko, George R

2018-01-15

Deep learning is a powerful tool that may allow for improved outcome prediction. To determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease (ARD) events and mortality in smokers. A CNN was trained using computed tomography scans from 7,983 COPDGene participants and evaluated using 1,000 nonoverlapping COPDGene participants and 1,672 ECLIPSE participants. Logistic regression (C statistic and the Hosmer-Lemeshow test) was used to assess COPD diagnosis and ARD prediction. Cox regression (C index and the Greenwood-Nam-D'Agnostino test) was used to assess mortality. In COPDGene, the C statistic for the detection of COPD was 0.856. A total of 51.1% of participants in COPDGene were accurately staged and 74.95% were within one stage. In ECLIPSE, 29.4% were accurately staged and 74.6% were within one stage. In COPDGene and ECLIPSE, the C statistics for ARD events were 0.64 and 0.55, respectively, and the Hosmer-Lemeshow P values were 0.502 and 0.380, respectively, suggesting no evidence of poor calibration. In COPDGene and ECLIPSE, CNN predicted mortality with fair discrimination (C indices, 0.72 and 0.60, respectively), and without evidence of poor calibration (Greenwood-Nam-D'Agnostino P values, 0.307 and 0.331, respectively). A deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.

Comparison of the effect of multiple intelligence pedagogy and traditional pedagogy on grade 5 students' achievement and attitudes towards science

NASA Astrophysics Data System (ADS)

Mussen, Kimberly S.

This quantitative research study evaluated the effectiveness of employing pedagogy based on the theory of multiple intelligences (MI). Currently, not all students are performing at the rate mandated by the government. When schools do not meet the required state standards, the school is labeled as not achieving adequate yearly progress (AYP), which may lead to the loss of funding. Any school not achieving AYP would be interested in this study. Due to low state standardized test scores in the district for science, student achievement and attitudes towards learning science were evaluated on a pretest, posttest, essay question, and one attitudinal survey. Statistical significance existed on one of the four research questions. Utilizing the Analysis of Covariance (ANCOVA) for data analysis, student attitudes towards learning science were statically significant in the MI (experimental) group. No statistical significance was found in student achievement on the posttest, delayed posttest, or the essay question test. Social change can result from this study because studying the effects of the multiple intelligence theory incorporated into classroom instruction can have significant effect on how children learn, allowing them to compete in a knowledge society.

How task characteristics and social support relate to managerial learning: empirical evidence from Dutch home care.

PubMed

Ouweneel, A P Else; Taris, Toon W; Van Zolingen, Simone J; Schreurs, Paul J G

2009-01-01

Researchers have revealed that managers profit most from informal and on-the-job learning. Moreover, research has shown that task characteristics and social support affect informal learning. On the basis of these insights, the authors examined the effects of task characteristics (psychological job demands, job control) and social support from the supervisor and colleagues on informal on-the-job learning among 1588 managers in the Dutch home-care sector. A regression analysis revealed that high demands, high control, and high colleague and supervisor support were each associated with high levels of informal learning. The authors found no evidence for statistical interactions among the effects of these concepts. They concluded that to promote managers' informal workplace learning, employers should especially increase job control.

On-line capacity-building program on "analysis of data" for medical educators in the South Asia region: a qualitative exploration of our experience.

PubMed

Dongre, A R; Chacko, T V; Banu, S; Bhandary, S; Sahasrabudhe, R A; Philip, S; Deshmukh, P R

2010-11-01

In medical education, using the World Wide Web is a new approach for building the capacity of faculty. However, there is little information available on medical education researchers' needs and their collective learning outcomes in such on-line environments. Hence, the present study attempted: 1)to identify needs for capacity-building of fellows in a faculty development program on the topic of data analysis; and 2) to describe, analyze and understand the collective learning outcomes of the fellows during this need-based on-line session. The present research is based on quantitative (on-line survey for needs assessment) and qualitative (contents of e-mails exchanged in listserv discussion) data which were generated during the October 2009 Mentoring and Learning (M-L) Web discussion on the topic of data analysis. The data sources were shared e-mail responses during the process of planning and executing the M-L Web discussion. Content analysis was undertaken and the categories of discussion were presented as a simple non-hierarchical typology which represents the collective learning of the project fellows. We identified the types of learning needs on the topic 'Analysis of Data' to be addressed for faculty development in the field of education research. This need-based M-L Web discussion could then facilitate collective learning on such topics as 'basic concepts in statistics', tests of significance, Likert scale analysis, bivariate correlation, and simple regression analysis and content analysis of qualitative data. Steps like identifying the learning needs for an on-line M-L Web discussion, addressing the immediate needs of learners and creating a flexible reflective learning environment on the M-L Web facilitated the collective learning of the fellows on the topic of data analysis. Our outcomes can be useful in the design of on-line pedagogical strategies for supporting research in medical education.

"Dear Fresher …"--How Online Questionnaires Can Improve Learning and Teaching Statistics

ERIC Educational Resources Information Center

Bebermeier, Sarah; Nussbeck, Fridtjof W.; Ontrup, Greta

2015-01-01

Lecturers teaching statistics are faced with several challenges supporting students' learning in appropriate ways. A variety of methods and tools exist to facilitate students' learning on statistics courses. The online questionnaires presented in this report are a new, slightly different computer-based tool: the central aim was to support students…

A Constructivist Approach in a Blended E-Learning Environment for Statistics

ERIC Educational Resources Information Center

Poelmans, Stephan; Wessa, Patrick

2015-01-01

In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…

Statistical Learning Effects in Musicians and Non-Musicians: An MEG Study

ERIC Educational Resources Information Center

Paraskevopoulos, Evangelos; Kuchenbuch, Anja; Herholz, Sibylle C.; Pantev, Christo

2012-01-01

This study aimed to assess the effect of musical training in statistical learning of tone sequences using Magnetoencephalography (MEG). Specifically, MEG recordings were used to investigate the neural and functional correlates of the pre-attentive ability for detection of deviance, from a statistically learned tone sequence. The effect of…

Influence of science and technology magnet middle schools on students' motivation and achievement in science

NASA Astrophysics Data System (ADS)

Allen, David

Some informal discussions among educators regarding motivation of students and academic performance have included the topic of magnet schools. The premise is that a focused theme, such as an aspect of science, positively affects student motivation and academic achievement. However, there is limited research involving magnet schools and their influence on student motivation and academic performance. This study provides empirical data for the discussion about magnet schools influence on motivation and academic ability. This study utilized path analysis in a structural equation modeling framework to simultaneously investigate the relationships between demographic exogenous independent variables, the independent variable of attending a science or technology magnet middle school, and the dependent variables of motivation to learn science and academic achievement in science. Due to the categorical nature of the variables, Bayesian statistical analysis was used to calculate the path coefficients and the standardized effects for each relationship in the model. The coefficients of determination were calculated to determine the amount of variance each path explained. Only five of 21 paths had statistical significance. Only one of the five statistically significant paths (Attended Magnet School to Motivation to Learn Science) explained a noteworthy amount (45.8%) of the variance.

Targeting change: Assessing a faculty learning community focused on increasing statistics content in life science curricula.

PubMed

Parker, Loran Carleton; Gleichsner, Alyssa M; Adedokun, Omolola A; Forney, James

2016-11-12

Transformation of research in all biological fields necessitates the design, analysis and, interpretation of large data sets. Preparing students with the requisite skills in experimental design, statistical analysis, and interpretation, and mathematical reasoning will require both curricular reform and faculty who are willing and able to integrate mathematical and statistical concepts into their life science courses. A new Faculty Learning Community (FLC) was constituted each year for four years to assist in the transformation of the life sciences curriculum and faculty at a large, Midwestern research university. Participants were interviewed after participation and surveyed before and after participation to assess the impact of the FLC on their attitudes toward teaching, perceived pedagogical skills, and planned teaching practice. Overall, the FLC had a meaningful positive impact on participants' attitudes toward teaching, knowledge about teaching, and perceived pedagogical skills. Interestingly, confidence for viewing the classroom as a site for research about teaching declined. Implications for the creation and development of FLCs for science faculty are discussed. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(6):517-525, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

PubMed

Daikoku, Tatsuya; Takahashi, Yuji; Futagami, Hiroko; Tarumoto, Nagayoshi; Yasuda, Hideki

2017-02-01

In real-world auditory environments, humans are exposed to overlapping auditory information such as those made by human voices and musical instruments even during routine physical activities such as walking and cycling. The present study investigated how concurrent physical exercise affects performance of incidental and intentional learning of overlapping auditory streams, and whether physical fitness modulates the performances of learning. Participants were grouped with 11 participants with lower and higher fitness each, based on their Vo 2 max value. They were presented simultaneous auditory sequences with a distinct statistical regularity each other (i.e. statistical learning), while they were pedaling on the bike and seating on a bike at rest. In experiment 1, they were instructed to attend to one of the two sequences and ignore to the other sequence. In experiment 2, they were instructed to attend to both of the two sequences. After exposure to the sequences, learning effects were evaluated by familiarity test. In the experiment 1, performance of statistical learning of ignored sequences during concurrent pedaling could be higher in the participants with high than low physical fitness, whereas in attended sequence, there was no significant difference in performance of statistical learning between high than low physical fitness. Furthermore, there was no significant effect of physical fitness on learning while resting. In the experiment 2, the both participants with high and low physical fitness could perform intentional statistical learning of two simultaneous sequences in the both exercise and rest sessions. The improvement in physical fitness might facilitate incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

Integrated Model for E-Learning Acceptance

NASA Astrophysics Data System (ADS)

Ramadiani; Rodziah, A.; Hasan, S. M.; Rusli, A.; Noraini, C.

2016-01-01

E-learning is not going to work if the system is not used in accordance with user needs. User Interface is very important to encourage using the application. Many theories had discuss about user interface usability evaluation and technology acceptance separately, actually why we do not make it correlation between interface usability evaluation and user acceptance to enhance e-learning process. Therefore, the evaluation model for e-learning interface acceptance is considered important to investigate. The aim of this study is to propose the integrated e-learning user interface acceptance evaluation model. This model was combined some theories of e-learning interface measurement such as, user learning style, usability evaluation, and the user benefit. We formulated in constructive questionnaires which were shared at 125 English Language School (ELS) students. This research statistics used Structural Equation Model using LISREL v8.80 and MANOVA analysis.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Transforming the advanced lab: Part I - Learning goals

NASA Astrophysics Data System (ADS)

Zwickl, Benjamin; Finkelstein, Noah; Lewandowski, H. J.

2012-02-01

Within the physics education research community relatively little attention has been given to laboratory courses, especially at the upper-division undergraduate level. As part of transforming our senior-level Optics and Modern Physics Lab at the University of Colorado Boulder we are developing learning goals, revising curricula, and creating assessments. In this paper, we report on the establishment of our learning goals and a surrounding framework that have emerged from discussions with a wide variety of faculty, from a review of the literature on labs, and from identifying the goals of existing lab courses. Our goals go beyond those of specific physics content and apparatus, allowing instructors to personalize them to their contexts. We report on four broad themes and associated learning goals: Modeling (math-physics-data connection, statistical error analysis, systematic error, modeling of engineered "black boxes"), Design (of experiments, apparatus, programs, troubleshooting), Communication, and Technical Lab Skills (computer-aided data analysis, LabVIEW, test and measurement equipment).

Socioscape: Real-Time Analysis of Dynamic Heterogeneous Networks In Complex Socio-Cultural Systems

DTIC Science & Technology

2015-10-22

Cluster Mixed-Membership Blockmodel for Time-Evolving Networks, Proceedings of the 14th International Conference on Artifical Intelligence and...Learning With Simultaneous Orthogonal Matching Pursuit, Proceedings of the 13th International Conference on Artifical Intelligence and Statistics

Evolution of social versus individual learning in a subdivided population revisited: comparative analysis of three coexistence mechanisms using the inclusive-fitness method.

PubMed

Kobayashi, Yutaka; Ohtsuki, Hisashi

2014-03-01

Learning abilities are categorized into social (learning from others) and individual learning (learning on one's own). Despite the typically higher cost of individual learning, there are mechanisms that allow stable coexistence of both learning modes in a single population. In this paper, we investigate by means of mathematical modeling how the effect of spatial structure on evolutionary outcomes of pure social and individual learning strategies depends on the mechanisms for coexistence. We model a spatially structured population based on the infinite-island framework and consider three scenarios that differ in coexistence mechanisms. Using the inclusive-fitness method, we derive the equilibrium frequency of social learners and the genetic load of social learning (defined as average fecundity reduction caused by the presence of social learning) in terms of some summary statistics, such as relatedness, for each of the three scenarios and compare the results. This comparative analysis not only reconciles previous models that made contradictory predictions as to the effect of spatial structure on the equilibrium frequency of social learners but also derives a simple mathematical rule that determines the sign of the genetic load (i.e. whether or not social learning contributes to the mean fecundity of the population). Copyright © 2013 Elsevier Inc. All rights reserved.

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

DOE PAGES

Rouet-Leduc, Bertrand; Barros, Kipton Marcos; Lookman, Turab; ...

2016-04-26

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.

The Impact of Individual Differences on E-Learning System Behavioral Intention

NASA Astrophysics Data System (ADS)

Liao, Peiwen; Yu, Chien; Yi, Chincheh

This study investigated the impact of contingent variables on the relationship between four predictors and employees' behavioral intention with e-learning. Seven hundred and twenty-two employees in online training and education were asked to answer questionnaires about their learning styles, perceptions of the quality of the proposed predictors and behavioral intention with e-learning systems. The results of analysis showed that three contingent variables, gender, job title and industry, significantly influenced the perceptions of predictors and employees' behavioral intention with the e-learning system. This study also found a statistically significant moderating effect of two contingent variables, gender, job title and industry, on the relationship between predictors and e-learning system behavioral intention. The results suggest that a serious consideration of contingent variables is crucial for improving e-learning system behavioral intention. The implications of these results for the management of e-learning systems are discussed.

The effect of directive tutor guidance on students' conceptual understanding of statistics in problem-based learning.

PubMed

Budé, Luc; van de Wiel, Margaretha W J; Imbos, Tjaart; Berger, Martijn P F

2011-06-01

Education is aimed at students reaching conceptual understanding of the subject matter, because this leads to better performance and application of knowledge. Conceptual understanding depends on coherent and error-free knowledge structures. The construction of such knowledge structures can only be accomplished through active learning and when new knowledge can be integrated into prior knowledge. The intervention in this study was directed at both the activation of students as well as the integration of knowledge. Undergraduate university students from an introductory statistics course, in an authentic problem-based learning (PBL) environment, were randomly assigned to conditions and measurement time points. In the PBL tutorial meetings, half of the tutors guided the discussions of the students in a traditional way. The other half guided the discussions more actively by asking directive and activating questions. To gauge conceptual understanding, the students answered open-ended questions asking them to explain and relate important statistical concepts. Results of the quantitative analysis show that providing directive tutor guidance improved understanding. Qualitative data of students' misconceptions seem to support this finding. Long-term retention of the subject matter seemed to be inadequate. ©2010 The British Psychological Society.

Engaging Underrepresented High School Students in Data Driven Storytelling: An Examination of Learning Experiences and Outcomes for a Cohort of Rising Seniors Enrolled in the Gaining Early Awareness and Readiness for Undergraduate Program (GEAR UP)

ERIC Educational Resources Information Center

Dierker, Lisa; Ward, Nadia; Alexander, Jalen; Donate, Emmanuel

2017-01-01

Background: Upward trends in data-oriented careers threaten to further increase the underrepresentation of both females and individuals from racial minority groups in programs focused on data analysis and applied statistics. To begin to develop the necessary skills for a data-oriented career, project-based learning seems the most promising given…

Neural network representation and learning of mappings and their derivatives

NASA Technical Reports Server (NTRS)

White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

1991-01-01

Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

The Role of the Company in Generating Skills. The Learning Effects of Work Organization. The Netherlands.

ERIC Educational Resources Information Center

Onstenk, Jeroen; Voncken, Eva

The impact of developments in work organizations on the skilling process in the Netherlands was studied through a macro analysis of available statistical information about the development of education for work in the Netherlands and case studies of three Dutch firms. The macro analysis focused on the following: vocational education in the…

Co-occurrence statistics as a language-dependent cue for speech segmentation.

PubMed

Saksida, Amanda; Langus, Alan; Nespor, Marina

2017-05-01

To what extent can language acquisition be explained in terms of different associative learning mechanisms? It has been hypothesized that distributional regularities in spoken languages are strong enough to elicit statistical learning about dependencies among speech units. Distributional regularities could be a useful cue for word learning even without rich language-specific knowledge. However, it is not clear how strong and reliable the distributional cues are that humans might use to segment speech. We investigate cross-linguistic viability of different statistical learning strategies by analyzing child-directed speech corpora from nine languages and by modeling possible statistics-based speech segmentations. We show that languages vary as to which statistical segmentation strategies are most successful. The variability of the results can be partially explained by systematic differences between languages, such as rhythmical differences. The results confirm previous findings that different statistical learning strategies are successful in different languages and suggest that infants may have to primarily rely on non-statistical cues when they begin their process of speech segmentation. © 2016 John Wiley & Sons Ltd.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

PubMed

Siegelman, Noam; Bogaerts, Louisa; Kronenfeld, Ofer; Frost, Ram

2017-10-07

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL. © 2017 Cognitive Science Society, Inc.

Explorations in Statistics: Standard Deviations and Standard Errors

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2008-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This series in "Advances in Physiology Education" provides an opportunity to do just that: we will investigate basic concepts in statistics using the free software package R. Because this series uses R solely as a vehicle…

Educational Statistics Authentic Learning CAPSULES: Community Action Projects for Students Utilizing Leadership and E-Based Statistics

ERIC Educational Resources Information Center

Thompson, Carla J.

2009-01-01

Since educational statistics is a core or general requirement of all students enrolled in graduate education programs, the need for high quality student engagement and appropriate authentic learning experiences is critical for promoting student interest and student success in the course. Based in authentic learning theory and engagement theory…

Examination of the Teaching Styles of Nursing Professional Development Specialists, Part II: Correlational Study on Teaching Styles and Use of Adult Learning Theory.

PubMed

Curran, Mary K

2014-07-16

This article, the second in a two-part series, details a correlational study that examined the effects of four variables (graduate degrees in nursing education, professional development training in adult learning theory, nursing professional development [NPD] certification, and NPD specialist experience) on the use of adult learning theory to guide curriculum development. Using the Principles of Adult Learning Scale, 114 NPD specialists tested the hypothesis that NPD specialists with graduate degrees in nursing education, professional development training in adult learning theory, NPD certification, and NPD experience would use higher levels of adult learning theory in their teaching practices to guide curriculum development than those without these attributes. This hypothesis was rejected as regression analysis revealed only one statistically significant predictor variable, NPD certification, influenced the use of adult learning theory. In addition, analysis revealed NPD specialists tended to support a teacher-centered rather than a learner-centered teaching style, indicating NPD educators are not using adult learning theory to guide teaching practices and curriculum development. J Contin Educ Nurs. 2014;45(8):xxx-xxx. Copyright 2014, SLACK Incorporated.

Dentists' attitude to provision of care for people with learning disabilities in Udaipur, India.

PubMed

Nagarajappa, Ramesh; Tak, Mridula; Sharda, Archana J; Asawa, Kailash; Jalihal, Sagar; Kakatkar, Gauri

2013-03-01

This study determines and compares the attitudes of dentists to the provision of care for people with learning disabilities according to gender, qualification, previous experience of treating patients with learning disabilities and work experience of dentists. A cross-sectional study was conducted among 247 dentists (166 men and 81 women) using a pretested structured questionnaire. This questionnaire assessed the respondent's attitude towards learning-disabled patients in five categories: beliefs about treating them, their capabilities, discrimination against these patients, their social behaviour and quality of care to be received by these patients. The information on dentist's gender, qualification, work experience and previous experience of treating patients with learning disabilities was also collected through questionnaire. The Student's t-test and anova test were used for statistical analysis. The mean attitude score was found to be 71.13 ± 8.97. A statistically significant difference was found in the mean attitude scores of dentists with work experience (p = 0.000). Study subjects with postgraduate qualification and previous experience of treating patients with learning disabilities had significantly greater mean attitude score than their counterparts (p = 0.000). The overall attitude of dentists towards provision of care for people with learning disabilities was favourable, which increased with higher qualification and past experience. © 2012 The Authors. Scandinavian Journal of Caring Sciences © 2012 Nordic College of Caring Science.

Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences.

PubMed

Koelsch, Stefan; Busch, Tobias; Jentschke, Sebastian; Rohrmeier, Martin

2016-02-02

Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. We carried out an EEG study with a novel variant of the established statistical learning paradigm. Timbres were presented in isochronous sequences of triplets. The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. Thus, the occurrence probability of the third item of each triplet (given the first two items) was varied. Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities.

Effect of Calendula officinalis hydroalcoholic extract on passive avoidance learning and memory in streptozotocin-induced diabetic rats

PubMed Central

Moradkhani, Shirin; Salehi, Iraj; Abdolmaleki, Somayeh; Komaki, Alireza

2015-01-01

Background: Medicinal plants, owing to their different mechanisms such as antioxidants effects, may improve learning and memory impairments in diabetic rats. Calendula officinalis (CO), has a significant antioxidant activity. Aims: To examine the effect of hydroalcoholic extract of CO on passive avoidance learning (PAL) and memory in streptozotocin (STZ)-induced diabetic male rats. Settings and Design: A total of 32 adult male Wistar rats were randomly allocated to four groups: Control, diabetic, control + extract of CO and diabetic control + extract of CO groups with free access to regular rat diet. Subjects and Methods: Diabetes in diabetic rats was induced by single intraperitoneal injection of 60 mg/kg STZ. After confirmation of diabetes, oral administration of 300 mg/kg CO extract to extract-treated groups have been done. PAL was tested 8 weeks after onset of treatment, and blood glucose and body weight were measured in all groups at the beginning and end of the experiment. Statistical Analysis Used: The statistical analysis of data was performed by ANOVA followed by least significant difference post-hoc analysis. Results: Diabetes decreased learning and memory. Effect of CO extract in retention test (after 24 and 48 h) has been shown a significant decrease in step-through latency and increase in time spent in the dark compartment part. Also the extract partially improved hyperglycemia and reduced body weight. Conclusion: Taken together, CO extract can improve PAL and memory impairments in STZ-diabetic rats. This improvement may be due to its antioxidant, anticholinergic activities or its power to reduce hyperglycemia. PMID:26120230

11.2 YIP Human In the Loop Statistical RelationalLearners

DTIC Science & Technology

2017-10-23

learning formalisms including inverse reinforcement learning [4] and statistical relational learning [7, 5, 8]. We have also applied our algorithms in...one introduced for label preferences. 4 Figure 2: Active Advice Seeking for Inverse Reinforcement Learning. active advice seeking is in selecting the...learning tasks. 1.2.1 Sequential Decision-Making Our previous work on advice for inverse reinforcement learning (IRL) defined advice as action

Fostering Creative Ecologies in Australasian Secondary Schools

ERIC Educational Resources Information Center

de Bruin, Leon R.; Harris, Anne

2017-01-01

This study investigates and compares elements of creativity in secondary schools and classrooms in Australia and Singapore. Statistical analysis and qualitative investigation of teacher, student and leadership perceptions of the emergence, fostering and absence of creativity in school learning environments is explored. This large-scale…

The Effects of Cooperative Learning and Feedback on E-Learning in Statistics

ERIC Educational Resources Information Center

Krause, Ulrike-Marie; Stark, Robin; Mandl, Heinz

2009-01-01

This study examined whether cooperative learning and feedback facilitate situated, example-based e-learning in the field of statistics. The factors "social context" (individual vs. cooperative) and "feedback intervention" (available vs. not available) were varied; participants were 137 university students. Results showed that…

Advanced building energy management system demonstration for Department of Defense buildings.

PubMed

O'Neill, Zheng; Bailey, Trevor; Dong, Bing; Shashanka, Madhusudana; Luo, Dong

2013-08-01

This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success. © 2013 New York Academy of Sciences.

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

NASA Astrophysics Data System (ADS)

Guo, Zhan; Yan, Xuefeng

2018-04-01

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Blended learning for reinforcing dental pharmacology in the clinical years: A qualitative analysis

PubMed Central

Eachempati, Prashanti; Kiran Kumar, K. S.; Sumanth, K. N.

2016-01-01

Objectives: Blended learning has become the method of choice in educational institutions because of its systematic integration of traditional classroom teaching and online components. This study aims to analyze student’s reflection regarding blended learning in dental pharmacology. Subjects and Methods: A cross-sectional study was conducted in Faculty of Dentistry, Melaka-Manipal Medical College among 3rd and 4th year BDS students. A total of 145 dental students, who consented, participate in the study. Students were divided into 14 groups. Nine online sessions followed by nine face-to-face discussions were held. Each session addressed topics related to oral lesions and orofacial pain with pharmacological applications. After each week, students were asked to reflect on blended learning. On completion of 9 weeks, reflections were collected and analyzed. Statistical Analysis: Qualitative analysis was done using thematic analysis model suggested by Braun and Clarke. Results: The four main themes were identified, namely, merits of blended learning, skill in writing prescription for oral diseases, dosages of drugs, and identification of strengths and weakness. In general, the participants had a positive feedback regarding blended learning. Students felt more confident in drug selection and prescription writing. They could recollect the doses better after the online and face-to-face sessions. Most interestingly, the students reflected that they are able to identify their strength and weakness after the blended learning sessions. Conclusions: Blended learning module was successfully implemented for reinforcing dental pharmacology. The results obtained in this study enable us to plan future comparative studies to know the effectiveness of blended learning in dental pharmacology. PMID:28031603

Using Guided Reinvention to Develop Teachers' Understanding of Hypothesis Testing Concepts

ERIC Educational Resources Information Center

Dolor, Jason; Noll, Jennifer

2015-01-01

Statistics education reform efforts emphasize the importance of informal inference in the learning of statistics. Research suggests statistics teachers experience similar difficulties understanding statistical inference concepts as students and how teacher knowledge can impact student learning. This study investigates how teachers reinvented an…

Statistical learning in social action contexts.

PubMed

Monroy, Claire; Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Statistical learning in social action contexts

PubMed Central

Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and—if so—whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together (‘Joint’ condition) or stated the intention to act alone (‘Parallel’ condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor’s action reliably predicted the second actor’s action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects. PMID:28475619

WINPEPI updated: computer programs for epidemiologists, and their teaching potential

PubMed Central

2011-01-01

Background The WINPEPI computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. The programs are free, and can be downloaded from the Internet. Numerous additions have been made in recent years. Implementation There are now seven WINPEPI programs: DESCRIBE, for use in descriptive epidemiology; COMPARE2, for use in comparisons of two independent groups or samples; PAIRSetc, for use in comparisons of paired and other matched observations; LOGISTIC, for logistic regression analysis; POISSON, for Poisson regression analysis; WHATIS, a "ready reckoner" utility program; and ETCETERA, for miscellaneous other procedures. The programs now contain 122 modules, each of which provides a number, sometimes a large number, of statistical procedures. The programs are accompanied by a Finder that indicates which modules are appropriate for different purposes. The manuals explain the uses, limitations and applicability of the procedures, and furnish formulae and references. Conclusions WINPEPI is a handy resource for a wide variety of statistical routines used by epidemiologists. Because of its ready availability, portability, ease of use, and versatility, WINPEPI has a considerable potential as a learning and teaching aid, both with respect to practical procedures in the planning and analysis of epidemiological studies, and with respect to important epidemiological concepts. It can also be used as an aid in the teaching of general basic statistics. PMID:21288353

Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences.

PubMed

Slone, Lauren Krogh; Johnson, Scott P

2015-05-01

Past research suggests that infants have powerful statistical learning abilities; however, studies of infants' visual statistical learning offer differing accounts of the developmental trajectory of and constraints on this learning. To elucidate this issue, the current study tested the hypothesis that young infants' segmentation of visual sequences depends on redundant statistical cues to segmentation. A sample of 20 2-month-olds and 20 5-month-olds observed a continuous sequence of looming shapes in which unit boundaries were defined by both transitional probability and co-occurrence frequency. Following habituation, only 5-month-olds showed evidence of statistically segmenting the sequence, looking longer to a statistically improbable shape pair than to a probable pair. These results reaffirm the power of statistical learning in infants as young as 5 months but also suggest considerable development of statistical segmentation ability between 2 and 5 months of age. Moreover, the results do not support the idea that infants' ability to segment visual sequences based on transitional probabilities and/or co-occurrence frequencies is functional at the onset of visual experience, as has been suggested previously. Rather, this type of statistical segmentation appears to be constrained by the developmental state of the learner. Factors contributing to the development of statistical segmentation ability during early infancy, including memory and attention, are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Midwifery education and technology enhanced learning: Evaluating online story telling in preregistration midwifery education.

PubMed

Scamell, Mandie; Hanley, Thomas

2018-03-01

A major issue regarding the implementation of blended learning for preregistration health programmes is the analysis of students' perceptions and attitudes towards their learning. It is the extent of the embedding of Technology Enhanced Learning (TEL) into the higher education curriculum that makes this analysis so vital. This paper reports on the quantitative results of a UK based study that was set up to respond to the apparent disconnect between technology enhanced education provision and reliable student evaluation of this mode of learning. Employing a mixed methods research design, the research described here was carried to develop a reliable and valid evaluation tool to measure acceptability of and satisfaction with a blended learning approach, specifically designed for a preregistration midwifery module offered at level 4. Feasibility testing of 46 completed blended learning evaluation questionnaires - Student Midwife Evaluation of Online Learning Effectiveness (SMEOLE) - using descriptive statistics, reliability and internal consistency tests. Standard deviations and mean scores all followed predicted pattern. Results from the reliability and internal consistency testing confirm the feasibility of SMEOLE as an effective tool for measuring student satisfaction with a blended learning approach to preregistration learning. The analysis presented in this paper suggests that we have been successful in our aim to produce an evaluation tool capable of assessing the quality of technology enhanced, University level learning in Midwifery. This work can provide future benchmarking against which midwifery, and other health, blended learning curriculum planning could be structured and evaluated. Copyright © 2017 Elsevier Ltd. All rights reserved.

Studying Student Benefits of Assigning a Service-Learning Project Compared to a Traditional Final Project in a Business Statistics Class

ERIC Educational Resources Information Center

Phelps, Amy L.; Dostilio, Lina

2008-01-01

The present study addresses the efficacy of using service-learning methods to meet the GAISE guidelines (http://www.amstat.org/education/gaise/GAISECollege.htm) in a second business statistics course and further explores potential advantages of assigning a service-learning (SL) project as compared to the traditional statistics project assignment.…

Competitive Processes in Cross-Situational Word Learning

PubMed Central

Yurovsky, Daniel; Yu, Chen; Smith, Linda B.

2013-01-01

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. But the information that learners pick up from these regularities is dependent on their learning mechanism. This paper investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input, and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales – both within and across trials/situations –learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is then considered from the perspective of a process-level understanding of cross-situational learning. PMID:23607610

Competitive processes in cross-situational word learning.

PubMed

Yurovsky, Daniel; Yu, Chen; Smith, Linda B

2013-07-01

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales-both within and across trials/situations-learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. Copyright © 2013 Cognitive Science Society, Inc.

Some Variables in Relation to Students' Anxiety in Learning Statistics.

ERIC Educational Resources Information Center

Sutarso, Toto

The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was…

Addressing astronomy misconceptions and achieving national science standards utilizing aspects of multiple intelligences theory in the classroom and the planetarium

NASA Astrophysics Data System (ADS)

Sarrazine, Angela Renee

The purpose of this study was to incorporate multiple intelligences techniques in both a classroom and planetarium setting to create a significant increase in student learning about the moon and lunar phases. Utilizing a free-response questionnaire and a 25 item multiple choice pre-test/post-test design, this study identified middle school students' misconceptions and measured increases in student learning about the moon and lunar phases. The study spanned two semesters and contained six treatment groups which consisted of both single and multiple interventions. One group only attended the planetarium program. Two groups attended one of two classes a week prior to the planetarium program, and two groups attended one of two classes a week after the planetarium program. The most rigorous treatment group attended a class both a week before and after the planetarium program. Utilizing Rasch analysis techniques and parametric statistical tests, all six groups exhibited statistically significant gains in knowledge at the 0.05 level. There were no significant differences between students who attended only a planetarium program versus a single classroom program. Also, subjects who attended either a pre-planetarium class or a post- planetarium class did not show a statistically significant gain over the planetarium only situation. Equivalent effects on student learning were exhibited by the pre-planetarium class groups and post-planetarium class groups. Therefore, it was determined that the placement of the second intervention does not have a significant impact on student learning. However, a decrease in learning was observed with the addition of a third intervention. Further instruction and testing appeared to hinder student learning. This is perhaps an effect of subject fatigue.

Best practices for measuring students' attitudes toward learning science.

PubMed

Lovelace, Matthew; Brickman, Peggy

2013-01-01

Science educators often characterize the degree to which tests measure different facets of college students' learning, such as knowing, applying, and problem solving. A casual survey of scholarship of teaching and learning research studies reveals that many educators also measure how students' attitudes influence their learning. Students' science attitudes refer to their positive or negative feelings and predispositions to learn science. Science educators use attitude measures, in conjunction with learning measures, to inform the conclusions they draw about the efficacy of their instructional interventions. The measurement of students' attitudes poses similar but distinct challenges as compared with measurement of learning, such as determining validity and reliability of instruments and selecting appropriate methods for conducting statistical analyses. In this review, we will describe techniques commonly used to quantify students' attitudes toward science. We will also discuss best practices for the analysis and interpretation of attitude data.

Best Practices for Measuring Students’ Attitudes toward Learning Science

PubMed Central

Lovelace, Matthew; Brickman, Peggy

2013-01-01

Science educators often characterize the degree to which tests measure different facets of college students’ learning, such as knowing, applying, and problem solving. A casual survey of scholarship of teaching and learning research studies reveals that many educators also measure how students’ attitudes influence their learning. Students’ science attitudes refer to their positive or negative feelings and predispositions to learn science. Science educators use attitude measures, in conjunction with learning measures, to inform the conclusions they draw about the efficacy of their instructional interventions. The measurement of students’ attitudes poses similar but distinct challenges as compared with measurement of learning, such as determining validity and reliability of instruments and selecting appropriate methods for conducting statistical analyses. In this review, we will describe techniques commonly used to quantify students’ attitudes toward science. We will also discuss best practices for the analysis and interpretation of attitude data. PMID:24297288

Supervised Learning for Dynamical System Learning.

PubMed

Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

2015-01-01

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

Peer Teaching to Foster Learning in Physiology.

PubMed

Srivastava, Tripti K; Waghmare, Lalitbhushan S; Mishra, Ved Prakash; Rawekar, Alka T; Quazi, Nazli; Jagzape, Arunita T

2015-08-01

Peer teaching is an effective tool to promote learning and retention of knowledge. By preparing to teach, students are encouraged to construct their own learning program, so that they can explain effectively to fellow learners. Peer teaching is introduced in present study to foster learning and pedagogical skills amongst first year medical under-graduates in physiology with a Hypothesis that teaching is linked to learning on part of the teacher. Non-randomized, Interventional study, with mixed methods design. Cases experienced peer teaching whereas controls underwent tutorials for four consecutive classes. Quantitative Evaluation was done through pre/post test score analysis for Class average normalized gain and tests of significance, difference in average score in surprise class test after one month and percentage of responses in closed ended items of feedback questionnaire. Qualitative Evaluation was done through categorization of open ended items and coding of reflective statements. The average pre and post test score was statistically significant within cases (p = 0.01) and controls (p = 0.023). The average post test scores was more for cases though not statistically significant. The class average normalized gain (g) for Tutorials was 49% and for peer teaching 53%. Surprise test had average scoring of 36 marks (out of 50) for controls and 41 marks for cases. Analysed section wise, the average score was better for Long answer question (LAQ) in cases. Section wise analysis suggested that through peer teaching, retention was better for descriptive answers as LAQ has better average score in cases. Feedback responses were predominantly positive for efficacy of peer teaching as a learning method. The reflective statements were sorted into reflection in action, reflection on action, claiming evidence, describing experience, and recognizing discrepancies. Teaching can stimulate further learning as it involves interplay of three processes: metacognitive awareness; deliberate practice, and self-explanation. Coupled with immediate feedback and reflective exercises, learning can be measurably enhanced along with improved teaching skills.

Peer Teaching to Foster Learning in Physiology

PubMed Central

Srivastava, Tripti K; Waghmare, Lalitbhushan S.; Mishra, Ved Prakash; Rawekar, Alka T; Quazi, Nazli; Jagzape, Arunita T

2015-01-01

Introduction Peer teaching is an effective tool to promote learning and retention of knowledge. By preparing to teach, students are encouraged to construct their own learning program, so that they can explain effectively to fellow learners. Peer teaching is introduced in present study to foster learning and pedagogical skills amongst first year medical under-graduates in physiology with a Hypothesis that teaching is linked to learning on part of the teacher. Materials and Methods Non-randomized, Interventional study, with mixed methods design. Cases experienced peer teaching whereas controls underwent tutorials for four consecutive classes. Quantitative Evaluation was done through pre/post test score analysis for Class average normalized gain and tests of significance, difference in average score in surprise class test after one month and percentage of responses in closed ended items of feedback questionnaire. Qualitative Evaluation was done through categorization of open ended items and coding of reflective statements. Results The average pre and post test score was statistically significant within cases (p = 0.01) and controls (p = 0.023). The average post test scores was more for cases though not statistically significant. The class average normalized gain (g) for Tutorials was 49% and for peer teaching 53%. Surprise test had average scoring of 36 marks (out of 50) for controls and 41 marks for cases. Analysed section wise, the average score was better for Long answer question (LAQ) in cases. Section wise analysis suggested that through peer teaching, retention was better for descriptive answers as LAQ has better average score in cases. Feedback responses were predominantly positive for efficacy of peer teaching as a learning method. The reflective statements were sorted into reflection in action, reflection on action, claiming evidence, describing experience, and recognizing discrepancies. Conclusion Teaching can stimulate further learning as it involves interplay of three processes: metacognitive awareness; deliberate practice, and self-explanation. Coupled with immediate feedback and reflective exercises, learning can be measurably enhanced along with improved teaching skills. PMID:26435969

Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

NASA Astrophysics Data System (ADS)

Zaborowicz, M.; Przybył, J.; Koszela, K.; Boniecki, P.; Mueller, W.; Raba, B.; Lewicki, A.; Przybył, K.

2014-04-01

The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

Functional differences between statistical learning with and without explicit training

PubMed Central

Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644

Blended Learning Versus Traditional Lecture in Introductory Nursing Pathophysiology Courses.

PubMed

Blissitt, Andrea Marie

2016-04-01

Currently, many undergraduate nursing courses use blended-learning course formats with success; however, little evidence exists that supports the use of blended formats in introductory pathophysiology courses. The purpose of this study was to compare the scores on pre- and posttests and course satisfaction between traditional and blended course formats in an introductory nursing pathophysiology course. This study used a quantitative, quasi-experimental, nonrandomized control group, pretest-posttest design. Analysis of covariance compared pre- and posttest scores, and a t test for independent samples compared students' reported course satisfaction of the traditional and blended course formats. Results indicated that the differences in posttest scores were not statistically significant between groups. Students in the traditional group reported statistically significantly higher satisfaction ratings than students in the blended group. The results of this study support the need for further research of using blended learning in introductory pathophysiology courses in undergraduate baccalaureate nursing programs. Further investigation into how satisfaction is affected by course formats is needed. Copyright 2016, SLACK Incorporated.

Statistical Analysis of Friendship Patterns and Bullying Behaviors among Youth

ERIC Educational Resources Information Center

Espelage, Dorothy L.; Green, Harold D., Jr.; Wasserman, Stanley

2007-01-01

During adolescence, friendship affiliations and groups provide companionship and social and emotional support, and they afford opportunities for intimate self-disclosure and reflection. Friendships often promote positive psychosocial development, but some youth learn and adopt antisocial attitudes and deviant behaviors through their friendships.…

Applications of Stochastic Analyses for Collaborative Learning and Cognitive Assessment

DTIC Science & Technology

2007-04-01

models (Visser, Maartje, Raijmakers, & Molenaar , 2002). The second part of this paper illustrates two applications of the methods described in the...clustering three-way data sets. Computational Statistics and Data Analysis, 51 (11), 5368–5376. Visser, I., Maartje, E., Raijmakers, E. J., & Molenaar

NEUROBEHAVIORAL EVALUATIONS OF BINARY AND TERTIARY MIXTURES OF CHEMICALS: LESSIONS LEARNING.

EPA Science Inventory

The classical approach to the statistical analysis of binary chemical mixtures is to construct full dose-response curves for one compound in the presence of a range of doses of the second compound (isobolographic analyses). For interaction studies using more than two chemicals, ...

Defining the learning curve in laparoscopic paraesophageal hernia repair: a CUSUM analysis.

PubMed

Okrainec, Allan; Ferri, Lorenzo E; Feldman, Liane S; Fried, Gerald M

2011-04-01

There are numerous reports in the literature documenting high recurrence rates after laparoscopic paraesophageal hernia repair. The purpose of this study was to determine the learning curve for this procedure using the Cumulative Summation (CUSUM) technique. Forty-six consecutive patients with paraesophageal hernia were evaluated prospectively after laparoscopic paraesophageal hernia repair. Upper GI series was performed 3 months postoperatively to look for recurrence. Patients were stratified based on the surgeon's early (first 20 cases) and late experience (>20 cases). The CUSUM method was then used to further analyze the learning curve. Nine patients (21%) had anatomic recurrence. There was a trend toward a higher recurrence rate during the first 20 cases, although this did not achieve statistical significance (33% vs. 13%, p = 0.10). However, using a CUSUM analysis to plot the learning curve, we found that the recurrence rate diminishes after 18 cases and reaches an acceptable rate after 26 cases. Surgeon experience is an important predictor of recurrence after laparoscopic paraesophageal hernia repair. CUSUM analysis revealed there is a significant learning curve to become proficient at this procedure, with approximately 20 cases required before a consistent decrease in hernia recurrence rate is observed.

"If You're Doubting Yourself Then, What's the Fun in That?" An Exploration of Why Prospective Secondary Mathematics Teachers Perceive Statistics as Difficult

ERIC Educational Resources Information Center

Leavy, Aisling M.; Hannigan, Ailish; Fitzmaurice, Olivia

2013-01-01

Most research into prospective secondary mathematics teachers' attitudes towards statistics indicates generally positive attitudes but a perception that statistics is difficult to learn. These perceptions of statistics as a difficult subject to learn may impact the approaches of prospective teachers to teaching statistics and in turn their…

Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model.

PubMed

Zhao, Xi; Dellandréa, Emmanuel; Chen, Liming; Kakadiaris, Ioannis A

2011-10-01

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.

Data-adaptive test statistics for microarray data.

PubMed

Mukherjee, Sach; Roberts, Stephen J; van der Laan, Mark J

2005-09-01

An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution. In this paper, we present a novel approach to the selection of differentially expressed genes in which test statistics are learned from data using a simple notion of reproducibility in selection results as the learning criterion. Reproducibility, as we define it, can be computed without any knowledge of the 'ground-truth', but takes advantage of certain properties of microarray data to provide an asymptotically valid guide to expected loss under the true data-generating distribution. We are therefore able to indirectly minimize expected loss, and obtain results substantially more robust than conventional methods. We apply our method to simulated and oligonucleotide array data. By request to the corresponding author.

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

PubMed

Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

2016-01-15

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

The Role of the Company in Generating Skills. The Learning Effects of Work Organization in the United Kingdom.

ERIC Educational Resources Information Center

Walsh, Kenneth; Green, Andy; Steedman, Hilary

The impact of developments in work organizations on the skilling process in the United Kingdom was studied through a macro analysis of available statistical information about the development of workplace training in the United Kingdom and case studies of three U.K. firms. The macro analysis focused on the following: initial training arrangements;…

Optical diagnosis of cervical cancer by higher order spectra and boosting

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Barman, Ritwik; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this contribution, we report the application of higher order statistical moments using decision tree and ensemble based learning methodology for the development of diagnostic algorithms for optical diagnosis of cancer. The classification results were compared to those obtained with an independent feature extractors like linear discriminant analysis (LDA). The performance and efficacy of these methodology using higher order statistics as a classifier using boosting has higher specificity and sensitivity while being much faster as compared to other time-frequency domain based methods.

Common statistical and research design problems in manuscripts submitted to high-impact psychiatry journals: what editors and reviewers want authors to know.

PubMed

Harris, Alex H S; Reeder, Rachelle; Hyun, Jenny K

2009-10-01

Journal editors and statistical reviewers are often in the difficult position of catching serious problems in submitted manuscripts after the research is conducted and data have been analyzed. We sought to learn from editors and reviewers of major psychiatry journals what common statistical and design problems they most often find in submitted manuscripts and what they wished to communicate to authors regarding these issues. Our primary goal was to facilitate communication between journal editors/reviewers and researchers/authors and thereby improve the scientific and statistical quality of research and submitted manuscripts. Editors and statistical reviewers of 54 high-impact psychiatry journals were surveyed to learn what statistical or design problems they encounter most often in submitted manuscripts. Respondents completed the survey online. The authors analyzed survey text responses using content analysis procedures to identify major themes related to commonly encountered statistical or research design problems. Editors and reviewers (n=15) who handle manuscripts from 39 different high-impact psychiatry journals responded to the survey. The most commonly cited problems regarded failure to map statistical models onto research questions, improper handling of missing data, not controlling for multiple comparisons, not understanding the difference between equivalence and difference trials, and poor controls in quasi-experimental designs. The scientific quality of psychiatry research and submitted reports could be greatly improved if researchers became sensitive to, or sought consultation on frequently encountered methodological and analytic issues.

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

A Modified Moore Approach to Teaching Mathematical Statistics: An Inquiry Based Learning Technique to Teaching Mathematical Statistics

ERIC Educational Resources Information Center

McLoughlin, M. Padraig M. M.

2008-01-01

The author of this paper submits the thesis that learning requires doing; only through inquiry is learning achieved, and hence this paper proposes a programme of use of a modified Moore method in a Probability and Mathematical Statistics (PAMS) course sequence to teach students PAMS. Furthermore, the author of this paper opines that set theory…

The Role of Statistical Learning and Working Memory in L2 Speakers' Pattern Learning

ERIC Educational Resources Information Center

McDonough, Kim; Trofimovich, Pavel

2016-01-01

This study investigated whether second language (L2) speakers' morphosyntactic pattern learning was predicted by their statistical learning and working memory abilities. Across three experiments, Thai English as a Foreign Language (EFL) university students (N = 140) were exposed to either the transitive construction in Esperanto (e.g., "tauro…

Introductory Statistics Students' Conceptual Understanding of Study Design and Conclusions

NASA Astrophysics Data System (ADS)

Fry, Elizabeth Brondos

Recommended learning goals for students in introductory statistics courses include the ability to recognize and explain the key role of randomness in designing studies and in drawing conclusions from those studies involving generalizations to a population or causal claims (GAISE College Report ASA Revision Committee, 2016). The purpose of this study was to explore introductory statistics students' understanding of the distinct roles that random sampling and random assignment play in study design and the conclusions that can be made from each. A study design unit lasting two and a half weeks was designed and implemented in four sections of an undergraduate introductory statistics course based on modeling and simulation. The research question that this study attempted to answer is: How does introductory statistics students' conceptual understanding of study design and conclusions (in particular, unbiased estimation and establishing causation) change after participating in a learning intervention designed to promote conceptual change in these areas? In order to answer this research question, a forced-choice assessment called the Inferences from Design Assessment (IDEA) was developed as a pretest and posttest, along with two open-ended assignments, a group quiz and a lab assignment. Quantitative analysis of IDEA results and qualitative analysis of the group quiz and lab assignment revealed that overall, students' mastery of study design concepts significantly increased after the unit, and the great majority of students successfully made the appropriate connections between random sampling and generalization, and between random assignment and causal claims. However, a small, but noticeable portion of students continued to demonstrate misunderstandings, such as confusion between random sampling and random assignment.

Enhancing interest in statistics among computer science students using computer tool entrepreneur role play

NASA Astrophysics Data System (ADS)

Judi, Hairulliza Mohamad; Sahari @ Ashari, Noraidah; Eksan, Zanaton Hj

2017-04-01

Previous research in Malaysia indicates that there is a problem regarding attitude towards statistics among students. They didn't show positive attitude in affective, cognitive, capability, value, interest and effort aspects although did well in difficulty. This issue should be given substantial attention because students' attitude towards statistics may give impacts on the teaching and learning process of the subject. Teaching statistics using role play is an appropriate attempt to improve attitudes to statistics, to enhance the learning of statistical techniques and statistical thinking, and to increase generic skills. The objectives of the paper are to give an overview on role play in statistics learning and to access the effect of these activities on students' attitude and learning in action research framework. The computer tool entrepreneur role play is conducted in a two-hour tutorial class session of first year students in Faculty of Information Sciences and Technology (FTSM), Universiti Kebangsaan Malaysia, enrolled in Probability and Statistics course. The results show that most students feel that they have enjoyable and great time in the role play. Furthermore, benefits and disadvantages from role play activities were highlighted to complete the review. Role play is expected to serve as an important activities that take into account students' experience, emotions and responses to provide useful information on how to modify student's thinking or behavior to improve learning.

Measuring Student Learning in Social Statistics: A Pretest-Posttest Study of Knowledge Gain

ERIC Educational Resources Information Center

Delucchi, Michael

2014-01-01

This study used a pretest-posttest design to measure student learning in undergraduate statistics. Data were derived from 185 students enrolled in six different sections of a social statistics course taught over a seven-year period by the same sociology instructor. The pretest-posttest instrument reveals statistically significant gains in…

Survey of Native English Speakers and Spanish-Speaking English Language Learners in Tertiary Introductory Statistics

ERIC Educational Resources Information Center

Lesser, Lawrence M.; Wagler, Amy E.; Esquinca, Alberto; Valenzuela, M. Guadalupe

2013-01-01

The framework of linguistic register and case study research on Spanish-speaking English language learners (ELLs) learning statistics informed the construction of a quantitative instrument, the Communication, Language, And Statistics Survey (CLASS). CLASS aims to assess whether ELLs and non-ELLs approach the learning of statistics differently with…

Statistical learning of multisensory regularities is enhanced in musicians: An MEG study.

PubMed

Paraskevopoulos, Evangelos; Chalas, Nikolas; Kartsidis, Panagiotis; Wollbrink, Andreas; Bamidis, Panagiotis

2018-07-15

The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni- and multi-modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non-musicians. The behavioral results indicated that the learning was successful for both musicians and non-musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschl's gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top-down processing. This neuroplastic effect showed a cross-modal stability between the auditory and audiovisual modalities. Copyright © 2018 Elsevier Inc. All rights reserved.

Active Learning with Statistical Models.

DTIC Science & Technology

1995-01-01

Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

Critical Analysis of an e-Learning and Interactive Teaching Module with Respect to the Interpretation of Emergency Computed Tomography of the Brain.

PubMed

Groth, Michael; Barthe, Käthe Greta; Riemer, Martin; Ernst, Marielle; Herrmann, Jochen; Fiehler, Jens; Buhk, Jan-Hendrik

2018-04-01

 To compare the learning benefit of three different teaching strategies on the interpretation of emergency cerebral computed tomography (CT) pathologies by medical students.  Three groups of students with different types of teaching (e-learning, interactive teaching, and standard curricular education in neuroradiology) were tested with respect to the detection of seven CT pathologies. The test results of each group were compared for each CT pathology using the chi-square test. A p-value ≤ 0.05 was considered to be significant.  Opposed to the results of the comparison group (curricular education), the e-learning group and interactive teaching tutorial group both showed a significantly better performance in detecting hyperdense middle cerebral artery sign (p = 0.001 and p < 0.0001) as well as subarachnoid hemorrhage (p = 0.03 and p = 0.001) on CT. Moreover, an increase in performance for the detection of subdural hematoma and skull fracture could be observed for both the interactive teaching group and the e-learning group, with statistical significance in the latter (p = 0.03 and p < 0.0001, respectively). No statistically significant differences were found for the detection of intracranial and epidural hemorrhage, as well as midline shift, among the groups studied.  Our study demonstrates potential learning benefits for both the interactive teaching tutorial and e-learning module group with respect to reading CT scans with slightly different advantages. Thus, the introduction of new learning methods in radiological education might be reasonable at an undergraduate stage but requires learning content-based considerations.   · E-learning can offer benefits regarding the reading of cerebral CT scans by students. · Interactive tutorial can offer benefits regarding the reading of cerebral CT scans by students. · E-learning and interactive tutorial feature different strengths for student learning in radiology. · Application of interactive teaching methods in radiology requires learning content-based considerations. · Groth M, Barthe KG, Riemer M et al. Critical Analysis of an e-Learning and Interactive Teaching Module with Respect to the Interpretation of Emergency Computed Tomography of the Brain. Fortschr Röntgenstr 2017; 190: 334 - 340. © Georg Thieme Verlag KG Stuttgart · New York.

Statistical word learning in children with autism spectrum disorder and specific language impairment.

PubMed

Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan

2017-11-01

Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of language status; however, fast-mapping abilities differ according to broader language skills. © 2017 Association for Child and Adolescent Mental Health.

The Impact of Congruency Between Preferred and Actual Learning Environments on Tenth Graders' Science Literacy in Taiwan

NASA Astrophysics Data System (ADS)

Chang, Chun-Yen; Yeh, Ting-Kuang; Lin, Chun-Yen; Chang, Yueh-Hsia; Chen, Chia-Li D.

2010-08-01

This study explored the effects of congruency between preferred and actual learning environment (PLE & ALE) perceptions on students' science literacy in terms of science concepts, attitudes toward science, and the understanding of the nature of science in an innovative curriculum of High Scope Project, namely Sci-Tech Mind and Humane Heart (STMHH). A pre-/post-treatment experiment was conducted with 34 Taiwanese tenth graders involved in this study. Participating students' preferred learning environment perception and pre-instruction scientific literacy were evaluated before the STMHH curriculum. Their perceptions toward the actual STMHH learning environment and post-instruction scientific literacy were also examined after the STMHH. Students were categorized into two groups; "preferred alignment with actual learning environment" (PAA) and "preferred discordant with actual learning environment" (PDA), according to their PLEI and ALEI scores. The results of this study revealed that most of the students in this study preferred learning in a classroom environment where student-centered and teacher-centered learning environments coexisted. Furthermore, the ANCOVA analysis showed marginally statistically significant difference between groups in terms of students' post-test scores on scientific literacy with the students' pre-test scores as the covariate. As a pilot study with a small sample size aiming to probe the research direction of this problem, the result of marginally statistically significant and approaching large sized effect magnitude is likely to implicate that the congruency between preferred and actual learning environments on students' scientific literacy is noteworthy. Future study of this nature appears to merit further replications and investigations.

Innovative intelligent technology of distance learning for visually impaired people

NASA Astrophysics Data System (ADS)

Samigulina, Galina; Shayakhmetova, Assem; Nuysuppov, Adlet

2017-12-01

The aim of the study is to develop innovative intelligent technology and information systems of distance education for people with impaired vision (PIV). To solve this problem a comprehensive approach has been proposed, which consists in the aggregate of the application of artificial intelligence methods and statistical analysis. Creating an accessible learning environment, identifying the intellectual, physiological, psychophysiological characteristics of perception and information awareness by this category of people is based on cognitive approach. On the basis of fuzzy logic the individually-oriented learning path of PIV is con- structed with the aim of obtaining high-quality engineering education with modern equipment in the joint use laboratories.

Correlation between musical aptitude and learning foreign languages: an epidemiological study in secondary school Italian students

PubMed Central

PICCIOTTI, P.M.; BUSSU, F.; CALò, L.; GALLUS, R.; SCARANO, E.; DI CINTIO, G.; CASSARÀ, F.; D’ALATRI, L.

2018-01-01

SUMMARY The aim of this study was to assess if a correlation exists between language learning skills and musical aptitude through the analysis of scholarly outcomes concerning the study of foreign languages and music. We enrolled 502 students from a secondary Italian school (10-14 years old), attending both traditional courses (2 hours/week of music classes scheduled) and special courses (six hours). For statistical analysis, we considered grades in English, French and Music. Our results showed a significant correlation between grades in the two foreign languages and in music, both in the traditional courses and in special courses, and better results in French than for special courses. These results are discussed and interpreted through the literature about neuroanatomical and physiological mechanisms of foreign language learning and music perception. PMID:29756615

Mere exposure alters category learning of novel objects.

PubMed

Folstein, Jonathan R; Gauthier, Isabel; Palmeri, Thomas J

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Mere Exposure Alters Category Learning of Novel Objects

PubMed Central

Folstein, Jonathan R.; Gauthier, Isabel; Palmeri, Thomas J.

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning. PMID:21833209

A Fishy Problem for Advanced Students

ERIC Educational Resources Information Center

Patterson, Richard A.

1977-01-01

While developing a research course for gifted high school students, improvements were made in a local pond. Students worked for a semester learning research techniques, statistical analysis, and limnology. At the end of the course, the three students produced a joint scientific paper detailing their study of the pond. (MA)

A Large-Scale Analysis of Variance in Written Language

ERIC Educational Resources Information Center

Johns, Brendan T.; Jamieson, Randall K.

2018-01-01

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers,…

The Science of the Individual

ERIC Educational Resources Information Center

Rose, L. Todd; Rouhani, Parisa; Fischer, Kurt W.

2013-01-01

Our goal is to establish a science of the individual, grounded in dynamic systems, and focused on the analysis of individual variability. Our argument is that individuals behave, learn, and develop in distinctive ways, showing patterns of variability that are not captured by models based on statistical averages. As such, any meaningful attempt to…

Emergent Readers' Social Interaction Styles and Their Comprehension Processes during Buddy Reading

ERIC Educational Resources Information Center

Christ, Tanya; Wang, X. Christine; Chiu, Ming Ming

2015-01-01

To examine the relations between emergent readers' social interaction styles and their comprehension processes, we adapted sociocultural and transactional views of learning and reading, and conducted statistical discourse analysis of 1,359 conversation turns transcribed from 14 preschoolers' 40 buddy reading events. Results show that interaction…

Collected Notes on the Workshop for Pattern Discovery in Large Databases

NASA Technical Reports Server (NTRS)

Buntine, Wray (Editor); Delalto, Martha (Editor)

1991-01-01

These collected notes are a record of material presented at the Workshop. The core data analysis is addressed that have traditionally required statistical or pattern recognition techniques. Some of the core tasks include classification, discrimination, clustering, supervised and unsupervised learning, discovery and diagnosis, i.e., general pattern discovery.

Developing and Assessing E-Learning Techniques for Teaching Forecasting

ERIC Educational Resources Information Center

Gel, Yulia R.; O'Hara Hines, R. Jeanette; Chen, He; Noguchi, Kimihiro; Schoner, Vivian

2014-01-01

In the modern business environment, managers are increasingly required to perform decision making and evaluate related risks based on quantitative information in the face of uncertainty, which in turn increases demand for business professionals with sound skills and hands-on experience with statistical data analysis. Computer-based training…

Leveraging Code Comments to Improve Software Reliability

ERIC Educational Resources Information Center

Tan, Lin

2009-01-01

Commenting source code has long been a common practice in software development. This thesis, consisting of three pieces of work, made novel use of the code comments written in natural language to improve software reliability. Our solution combines Natural Language Processing (NLP), Machine Learning, Statistics, and Program Analysis techniques to…

Digital Learning Compass: Distance Education State Almanac 2017. Delaware

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Delaware. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Kansas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Kansas. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Minnesota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Minnesota. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Utah

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Utah. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Connecticut

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Connecticut. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Wyoming

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Wyoming. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Montana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Montana. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Iowa

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Iowa. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Alabama

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Alabama. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Nevada

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Nevada. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Mississippi

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Mississippi. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Kentucky

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Kentucky. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Ohio

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Ohio. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Oklahoma

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oklahoma. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Texas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Texas. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Vermont

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Vermont. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Colorado

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Colorado. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Arizona

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Arizona . The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Missouri

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Missouri. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Idaho

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Idaho. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Massachusetts

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Massachusetts. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Tennessee

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Tennessee. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Virginia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Virginia. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Indiana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Indiana. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Alaska

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Alaska. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Louisiana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Louisiana. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Nebraska

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Nebraska. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Maine

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Maine. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Wisconsin

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Wisconsin. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Michigan

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Michigan. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Arkansas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Arkansas . The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Illinois

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Illinois. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Florida

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Florida. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Maryland

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Maryland. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Oregon

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oregon. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Washington

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Washington. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Hawaii

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Hawaii. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. California

ERIC Educational Resources Information Center

Seaman, Julia A.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of California. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Georgia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Georgia. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Pennsylvania

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Pennsylvania. The sample for this analysis is comprised of all active, degree-granting…

A MOOC on Approaches to Machine Translation

ERIC Educational Resources Information Center

Costa-jussà, Mart R.; Formiga, Lluís; Torrillas, Oriol; Petit, Jordi; Fonollosa, José A. R.

2015-01-01

This paper describes the design, development, and analysis of a MOOC entitled "Approaches to Machine Translation: Rule-based, statistical and hybrid", and provides lessons learned and conclusions to be taken into account in the future. The course was developed within the Canvas platform, used by recognized European universities. It…

How to Engage Medical Students in Chronobiology: An Example on Autorhythmometry

ERIC Educational Resources Information Center

Rol de Lama, M. A.; Lozano, J. P.; Ortiz, V.; Sanchez-Vazquez, F. J.; Madrid, J. A.

2005-01-01

This contribution describes a new laboratory experience that improves medical students' learning of chronobiology by introducing them to basic chronobiology concepts as well as to methods and statistical analysis tools specific for circadian rhythms. We designed an autorhythmometry laboratory session where students simultaneously played the role…

Real-world visual statistics and infants' first-learned object names

PubMed Central

Clerkin, Elizabeth M.; Hart, Elizabeth; Rehg, James M.; Yu, Chen

2017-01-01

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present—a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872373

Statistical learning of action: the role of conditional probability.

PubMed

Meyer, Meredith; Baldwin, Dare

2011-12-01

Identification of distinct units within a continuous flow of human action is fundamental to action processing. Such segmentation may rest in part on statistical learning. In a series of four experiments, we examined what types of statistics people can use to segment a continuous stream involving many brief, goal-directed action elements. The results of Experiment 1 showed no evidence for sensitivity to conditional probability, whereas Experiment 2 displayed learning based on joint probability. In Experiment 3, we demonstrated that additional exposure to the input failed to engender sensitivity to conditional probability. However, the results of Experiment 4 showed that a subset of adults-namely, those more successful at identifying actions that had been seen more frequently than comparison sequences-were also successful at learning conditional-probability statistics. These experiments help to clarify the mechanisms subserving processing of intentional action, and they highlight important differences from, as well as similarities to, prior studies of statistical learning in other domains, including language.

Chem-2-Chem: A One-to-One Supportive Learning Environment for Chemistry

NASA Astrophysics Data System (ADS)

Báez-Galib, Rosita; Colón-Cruz, Héctor; Resto, Wilfredo; Rubin, Michael R.

2005-12-01

The Chem-2-Chem (C2C) tutoring mentoring program was developed at the University of Puerto Rico at Cayey, an undergraduate institution serving Hispanic students, to increase student retention and help students achieve successful general chemistry course outcomes. This program provides a supportive learning environment designed to address students' academic and emotional needs in a holistic way. Advanced chemistry students offered peer-led, personalized, and individualized learning experiences through tutoring and mentoring to approximately 21% of students enrolled in the general chemistry course. Final grades from official class lists of all general chemistry course sections were analyzed using Student's t -test, paired t -test, and χ 2 analysis. Results during the seven semesters studied show an increase of 29% in successful course outcomes defined as final letter grades of A, B, and C obtained by Chem-2-Chem participants. For each final grade, highly statistically significant differences between participants and nonparticipants were detected. There were also statistically significant differences between successful course outcomes obtained by participants and nonparticipants for each of the semesters studied. This research supports recent trends in chemical education to provide a social context for learning experiences. This peer-led learning strategy can serve as an effective model to achieve excellence in science courses at a wide range of educational institutions.

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

PubMed Central

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.

2014-01-01

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data. PMID:25431544

Novelty or knowledge? A study of using a student response system in non-major biology courses at a community college

NASA Astrophysics Data System (ADS)

Thames, Tasha Herrington

The advancement in technology integration is laying the groundwork of a paradigm shift in the higher education system (Noonoo, 2011). The National Dropout Prevention Center (n.d.) claims that technology offers some of the best opportunities for presenting instruction to engage students in meaningful education, addressing multiple intelligences, and adjusting to students' various learning styles. The purpose of this study was to investigate if implementing clicker technology would have a statistically significant difference on student retention and student achievement, while controlling for learning styles, for students in non-major biology courses who were and were not subjected to the technology. This study also sought to identify if students perceived the use of clickers as beneficial to their learning. A quantitative quasi-experimental research design was utilized to determine the significance of differences in pre/posttest achievement scores between students who participated during the fall semester in 2014. Overall, 118 students (n = 118) voluntarily enrolled in the researcher's fall non-major Biology course at a southern community college. A total of 71 students were assigned to the experimental group who participated in instruction incorporating the ConcepTest Process with clicker technology along with traditional lecture. The remaining 51 students were assigned to the control group who participated in a traditional lecture format with peer instruction embedded. Statistical analysis revealed the experimental clicker courses did have higher posttest scores than the non-clicker control courses, but this was not significant (p >.05). Results also implied that clickers did not statistically help retain students to complete the course. Lastly, the results indicated that there were no significant statistical difference in student's clicker perception scores between the different learning style preferences.

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities.

PubMed

Gutiérrez, David; Ramírez-Moreno, Mauricio A

2016-04-01

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of [Formula: see text] and [Formula: see text] rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.

Waveform classification and statistical analysis of seismic precursors to the July 2008 Vulcanian Eruption of Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Rodgers, Mel; Smith, Patrick; Pyle, David; Mather, Tamsin

2016-04-01

Understanding the transition between quiescence and eruption at dome-forming volcanoes, such as Soufrière Hills Volcano (SHV), Montserrat, is important for monitoring volcanic activity during long-lived eruptions. Statistical analysis of seismic events (e.g. spectral analysis and identification of multiplets via cross-correlation) can be useful for characterising seismicity patterns and can be a powerful tool for analysing temporal changes in behaviour. Waveform classification is crucial for volcano monitoring, but consistent classification, both during real-time analysis and for retrospective analysis of previous volcanic activity, remains a challenge. Automated classification allows consistent re-classification of events. We present a machine learning (random forest) approach to rapidly classify waveforms that requires minimal training data. We analyse the seismic precursors to the July 2008 Vulcanian explosion at SHV and show systematic changes in frequency content and multiplet behaviour that had not previously been recognised. These precursory patterns of seismicity may be interpreted as changes in pressure conditions within the conduit during magma ascent and could be linked to magma flow rates. Frequency analysis of the different waveform classes supports the growing consensus that LP and Hybrid events should be considered end members of a continuum of low-frequency source processes. By using both supervised and unsupervised machine-learning methods we investigate the nature of waveform classification and assess current classification schemes.

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

PubMed Central

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-01-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

PubMed

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-28

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Automated Clinical Assessment from Smart home-based Behavior Data

PubMed Central

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-01-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

NASA Astrophysics Data System (ADS)

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Blended learning in ethics education: a survey of nursing students.

PubMed

Hsu, Li-Ling

2011-05-01

Nurses are experiencing new ethical issues as a result of global developments and changes in health care. With health care becoming increasingly sophisticated, and countries facing challenges of graying population, ethical issues involved in health care are bound to expand in quantity and in depth. Blended learning rather as a combination of multiple delivery media designed to promote meaningful learning. Specifically, this study was focused on two questions: (1) the students' satisfaction and attitudes as members of a scenario-based learning process in a blended learning environment; (2) the relationship between students' satisfaction ratings of nursing ethics course and their attitudes in the blended learning environment. In total, 99 senior undergraduate nursing students currently studying at a public nursing college in Taiwan were invited to participate in this study. A cross-sectional survey design was adopted in this study. The participants were asked to fill out two Likert-scale questionnaire surveys: CAAS (Case Analysis Attitude Scale), and BLSS (Blended Learning Satisfaction Scale). The results showed what students felt about their blended learning experiences - mostly items ranged from 3.27-3.76 (the highest score is 5). Another self-assessment of scenario analysis instrument revealed the mean scores ranged from 2.87-4.19. Nearly 57.8% of the participants rated the course 'extremely helpful' or 'very helpful.' This study showed statistically significant correlations (r=0.43) between students' satisfaction with blended learning and case analysis attitudes. In addition, results testified to a potential of the blended learning model proposed in this study to bridge the gap between students and instructors and the one between students and their peers, which are typical of blended learning, and to create meaningful learning by employing blended pedagogical consideration in the course design. The use of scenario instruction enables students to develop critical analysis and problem solving skills through active learning and social exchange of ideas. © The Author(s) 2011

A Primer on the Statistical Modelling of Learning Curves in Health Professions Education

ERIC Educational Resources Information Center

Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y.

2017-01-01

Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…

Students' Perceptions of Computer-Based Learning Environments, Their Attitude towards Business Statistics, and Their Academic Achievement: Implications from a UK University

ERIC Educational Resources Information Center

Nguyen, ThuyUyen H.; Charity, Ian; Robson, Andrew

2016-01-01

This study investigates students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement in higher education. Guided by learning environments concepts and attitudinal theory, a theoretical model was proposed with two instruments, one for measuring the learning environment and…

Content, Affective, and Behavioral Challenges to Learning: Students' Experiences Learning Statistics

ERIC Educational Resources Information Center

McGrath, April L.

2014-01-01

This study examined the experiences of and challenges faced by students when completing a statistics course. As part of the requirement for this course, students completed a learning check-in, which consisted of an individual meeting with the instructor to discuss questions and the completion of a learning reflection and study plan. Forty…

Finnish Upper Secondary Students' Collaborative Processes in Learning Statistics in a CSCL Environment

ERIC Educational Resources Information Center

Oikarinen, Juho Kaleva; Järvelä, Sanna; Kaasila, Raimo

2014-01-01

This design-based research project focuses on documenting statistical learning among 16-17-year-old Finnish upper secondary school students (N = 78) in a computer-supported collaborative learning (CSCL) environment. One novel value of this study is in reporting the shift from teacher-led mathematical teaching to autonomous small-group learning in…

Predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil

PubMed Central

Czepula, Alexandra I.; Bottacin, Wallace E.; Hipólito, Edson; Baptista, Deise R.; Pontarolo, Roberto; Correr, Cassyano J.

2015-01-01

Background: Learning styles are cognitive, emotional, and physiological traits, as well as indicators of how learners perceive, interact, and respond to their learning environments. According to Honey-Mumford, learning styles are classified as active, reflexive, theoretical, and pragmatic. Objective: The purpose of this study was to identify the predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil. Methods: An observational, cross-sectional, and descriptive study was conducted using the Honey-Alonso Learning Style Questionnaire. Students in the Bachelor of Pharmacy program were invited to participate in this study. The questionnaire comprised 80 randomized questions, 20 for each of the four learning styles. The maximum possible score was 20 points for each learning style, and cumulative scores indicated the predominant learning styles among the participants. Honey-Mumford (1986) proposed five preference levels for each style (very low, low, moderate, high, and very high), called a general interpretation scale, to avoid student identification with one learning style and ignoring the characteristics of the other styles. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 20.0. Results: This study included 297 students (70% of all pharmacy students at the time) with a median age of 21 years old. Women comprised 77.1% of participants. The predominant style among pharmacy students at the Federal University of Paraná was the pragmatist, with a median of 14 (high preference). The pragmatist style prevails in people who are able to discover techniques related to their daily learning because such people are curious to discover new strategies and attempt to verify whether the strategies are efficient and valid. Because these people are direct and objective in their actions, pragmatists prefer to focus on practical issues that are validated and on problem situations. There was no statistically significant difference between genders with regard to learning styles. Conclusion: The pragmatist style is the prevailing style among pharmacy students at the Federal University of Paraná. Although students may have a learning preference that preference is not the only manner in which students can learn, neither their preference is the only manner in which students can be taught. Awareness of students’ learning styles can be used to adapt the methodology used by teachers to render the teaching-learning process effective and long lasting. The content taught to students should be presented in different manners because varying teaching methods can develop learning skills in students. PMID:27011774

Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

PubMed

Ma, Ning; Yu, Angela J

2015-01-01

Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

Can You Explain that in Plain English? Making Statistics Group Projects Work in a Multicultural Setting

ERIC Educational Resources Information Center

Sisto, Michelle

2009-01-01

Students increasingly need to learn to communicate statistical results clearly and effectively, as well as to become competent consumers of statistical information. These two learning goals are particularly important for business students. In line with reform movements in Statistics Education and the GAISE guidelines, we are working to implement…

Designing a Course in Statistics for a Learning Health Systems Training Program

ERIC Educational Resources Information Center

Samsa, Gregory P.; LeBlanc, Thomas W.; Zaas, Aimee; Howie, Lynn; Abernethy, Amy P.

2014-01-01

The core pedagogic problem considered here is how to effectively teach statistics to physicians who are engaged in a "learning health system" (LHS). This is a special case of a broader issue--namely, how to effectively teach statistics to academic physicians for whom research--and thus statistics--is a requirement for professional…

Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

NASA Astrophysics Data System (ADS)

Kim, Dong-Youl; Lee, Jong-Hwan

2014-05-01

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

Smoking Gun or Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants.

PubMed

Gagliano, Sarah A; Ravji, Reena; Barnes, Michael R; Weale, Michael E; Knight, Jo

2015-08-24

Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.

Living Animals in the Classroom: A Meta-Analysis on Learning Outcome and a Treatment-Control Study Focusing on Knowledge and Motivation

ERIC Educational Resources Information Center

Hummel, Eberhard; Randler, Christoph

2012-01-01

Prior research states that the use of living animals in the classroom leads to a higher knowledge but those previous studies have methodological and statistical problems. We applied a meta-analysis and developed a treatment-control study in a middle school classroom. The treatments (film vs. living animal) differed only by the presence of the…

Assessment of a Learning Strategy among Spine Surgeons.

PubMed

Gotfryd, Alberto Ofenhejm; Corredor, Jose Alfredo; Teixeira, William Jacobsen; Martins, Delio Eulálio; Milano, Jeronimo; Iutaka, Alexandre Sadao

2017-02-01

Pilot test, observational study. To evaluate objectively the knowledge transfer provided by theoretical and practical activities during AOSpine courses for spine surgeons. During two AOSpine principles courses, 62 participants underwent precourse assessment, which consisted of questions about their professional experience, preferences regarding adolescent idiopathic scoliosis (AIS) classification, and classifying the curves by means of the Lenke classification of two AIS clinical cases. Two learning strategies were used during the course. A postcourse questionnaire was applied to reclassify the same deformity cases. Differences in the correct answers of clinical cases between pre- and postcourse were analyzed, revealing the number of participants whose accuracy in classification improved after the course. Analysis showed a decrease in the number of participants with wrong answers in both cases after the course. In the first case, statistically significant differences were observed in both curve pattern (83.3%, p   =  0.005) and lumbar spine modifier (46.6%, p   =  0.049). No statistically significant improvement was seen in the sagittal thoracic modifier (33.3%, p   =  0.309). In the second case, statistical improvement was obtained in curve pattern (27.4%, p   =  0.018). No statistically significant improvement was seen regarding lumbar spine modifier (9.8%, p   =  0.121) and sagittal thoracic modifier (12.9%, p   =  0.081). This pilot test showed objectively that learning strategies used during AOSpine courses improved the participants' knowledge. Teaching strategies must be continually improved to ensure an optimal level of knowledge transfer.

Assessment of a Learning Strategy among Spine Surgeons

PubMed Central

Gotfryd, Alberto Ofenhejm; Teixeira, William Jacobsen; Martins, Delio Eulálio; Milano, Jeronimo; Iutaka, Alexandre Sadao

2017-01-01

Study Design Pilot test, observational study. Objective To evaluate objectively the knowledge transfer provided by theoretical and practical activities during AOSpine courses for spine surgeons. Methods During two AOSpine principles courses, 62 participants underwent precourse assessment, which consisted of questions about their professional experience, preferences regarding adolescent idiopathic scoliosis (AIS) classification, and classifying the curves by means of the Lenke classification of two AIS clinical cases. Two learning strategies were used during the course. A postcourse questionnaire was applied to reclassify the same deformity cases. Differences in the correct answers of clinical cases between pre- and postcourse were analyzed, revealing the number of participants whose accuracy in classification improved after the course. Results Analysis showed a decrease in the number of participants with wrong answers in both cases after the course. In the first case, statistically significant differences were observed in both curve pattern (83.3%, p  =  0.005) and lumbar spine modifier (46.6%, p  =  0.049). No statistically significant improvement was seen in the sagittal thoracic modifier (33.3%, p  =  0.309). In the second case, statistical improvement was obtained in curve pattern (27.4%, p  =  0.018). No statistically significant improvement was seen regarding lumbar spine modifier (9.8%, p  =  0.121) and sagittal thoracic modifier (12.9%, p  =  0.081). Conclusion This pilot test showed objectively that learning strategies used during AOSpine courses improved the participants' knowledge. Teaching strategies must be continually improved to ensure an optimal level of knowledge transfer. PMID:28451507

Chromosomal microarray analysis of consecutive individuals with autism spectrum disorders or learning disability presenting for genetic services.

PubMed

Roberts, Jennifer L; Hovanes, Karine; Dasouki, Majed; Manzardo, Ann M; Butler, Merlin G

2014-02-01

Chromosomal microarray analysis is now commonly used in clinical practice to identify copy number variants (CNVs) in the human genome. We report our experience with the use of the 105 K and 180K oligonucleotide microarrays in 215 consecutive patients referred with either autism or autism spectrum disorders (ASD) or developmental delay/learning disability for genetic services at the University of Kansas Medical Center during the past 4 years (2009-2012). Of the 215 patients [140 males and 75 females (male/female ratio=1.87); 65 with ASD and 150 with learning disability], abnormal microarray results were seen in 45 individuals (21%) with a total of 49 CNVs. Of these findings, 32 represented a known diagnostic CNV contributing to the clinical presentation and 17 represented non-diagnostic CNVs (variants of unknown significance). Thirteen patients with ASD had a total of 14 CNVs, 6 CNVs recognized as diagnostic and 8 as non-diagnostic. The most common chromosome involved in the ASD group was chromosome 15. For those with a learning disability, 32 patients had a total of 35 CNVs. Twenty-six of the 35 CNVs were classified as a known diagnostic CNV, usually a deletion (n=20). Nine CNVs were classified as an unknown non-diagnostic CNV, usually a duplication (n=8). For the learning disability subgroup, chromosomes 2 and 22 were most involved. Thirteen out of 65 patients (20%) with ASD had a CNV compared with 32 out of 150 patients (21%) with a learning disability. The frequency of chromosomal microarray abnormalities compared by subject group or gender was not statistically different. A higher percentage of individuals with a learning disability had clinical findings of seizures, dysmorphic features and microcephaly, but not statistically significant. While both groups contained more males than females, a significantly higher percentage of males were present in the ASD group. © 2013 Elsevier B.V. All rights reserved.

Using rock art as an alternative science pedagogy

NASA Astrophysics Data System (ADS)

Allen, Casey D.

College-level and seventh-grade science students were studied to understand the power of a field index, the Rock Art Stability Index (RASI), for student learning about complex biophysical environmental processes. In order to determine if the studied population was representative, 584 college and seventh-grade students undertook a concept mapping exercise after they had learned basic weathering science via in-class lecture. Of this large group, a subset of 322 college students and 13 seventh-grade students also learned RASI through a field experience involving the analysis of rock weathering associated with petroglyphs. After learning weathering through RASI, students completed another concept map. This was a college population where roughly 46% had never taken a "lab science" course and nearly 22% were from minority (non-white) populations. Analysis of student learning through the lens of actor-network theory revealed that when landscape is viewed as process (i.e. many practices), science education embodies both an alternative science philosophy and an alternative materialistic worldview. When RASI components were analyzed after only lecture, student understanding of weathering displayed little connection between weathering form and weathering process. After using RASI in the field however, nearly all students made illustrative concept maps rich in connections between weathering form and weathering process for all subcomponents of RASI. When taken as an aggregate, and measured by an average concept map score, learning increased by almost 14%, Among college minority students, the average score increase approached 23%. Among female students, the average score increase was 16%. For seventh-grade students, scores increased by nearly 36%. After testing for normalcy with Kolmogorov-Smirnov, t-tests reveal that all of these increases were highly statistically significant at p<0.001. The growth in learning weathering science by minority students, as compared to non-minority students, was also statistically significant at p<0.01. These findings reveal the power of field work through RASI to strengthen cognitive linkages between complex biophysical processes and the corresponding rock weathering forms.

Domain General Constraints on Statistical Learning

ERIC Educational Resources Information Center

Thiessen, Erik D.

2011-01-01

All theories of language development suggest that learning is constrained. However, theories differ on whether these constraints arise from language-specific processes or have domain-general origins such as the characteristics of human perception and information processing. The current experiments explored constraints on statistical learning of…

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge.

PubMed

Daikoku, Tatsuya

2018-01-01

Learning and knowledge of transitional probability in sequences like music, called statistical learning and knowledge, are considered implicit processes that occur without intention to learn and awareness of what one knows. This implicit statistical knowledge can be alternatively expressed via abstract medium such as musical melody, which suggests this knowledge is reflected in melodies written by a composer. This study investigates how statistics in music vary over a composer's lifetime. Transitional probabilities of highest-pitch sequences in Ludwig van Beethoven's Piano Sonata were calculated based on different hierarchical Markov models. Each interval pattern was ordered based on the sonata opus number. The transitional probabilities of sequential patterns that are musical universal in music gradually decreased, suggesting that time-course variations of statistics in music reflect time-course variations of a composer's statistical knowledge. This study sheds new light on novel methodologies that may be able to evaluate the time-course variation of composer's implicit knowledge using musical scores.

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

PubMed Central

Turk-Browne, Nicholas B.; Botvinick, Matthew M.; Norman, Kenneth A.

2017-01-01

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway—the pathway connecting entorhinal cortex directly to region CA1—was able to support statistical learning, while the trisynaptic pathway—connecting entorhinal cortex to CA1 through dentate gyrus and CA3—learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872368

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

PubMed

Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M; Norman, Kenneth A

2017-01-05

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering.

PubMed

Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato

2017-01-27

Previous neural studies have supported the hypothesis that statistical learning mechanisms are used broadly across different domains such as language and music. However, these studies have only investigated a single aspect of statistical learning at a time, such as recognizing word boundaries or learning word order patterns. In this study, we neutrally investigated how the two levels of statistical learning for recognizing word boundaries and word ordering could be reflected in neuromagnetic responses and how acquired statistical knowledge is reorganised when the syntactic rules are revised. Neuromagnetic responses to the Japanese-vowel sequence (a, e, i, o, and u), presented every .45s, were recorded from 14 right-handed Japanese participants. The vowel order was constrained by a Markov stochastic model such that five nonsense words (aue, eao, iea, oiu, and uoi) were chained with an either-or rule: the probability of the forthcoming word was statistically defined (80% for one word; 20% for the other word) by the most recent two words. All of the word transition probabilities (80% and 20%) were switched in the middle of the sequence. In the first and second quarters of the sequence, the neuromagnetic responses to the words that appeared with higher transitional probability were significantly reduced compared with those that appeared with a lower transitional probability. After switching the word transition probabilities, the response reduction was replicated in the last quarter of the sequence. The responses to the final vowels in the words were significantly reduced compared with those to the initial vowels in the last quarter of the sequence. The results suggest that both within-word and between-word statistical learning are reflected in neural responses. The present study supports the hypothesis that listeners learn larger structures such as phrases first, and they subsequently extract smaller structures, such as words, from the learned phrases. The present study provides the first neurophysiological evidence that the correction of statistical knowledge requires more time than the acquisition of new statistical knowledge. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of the quality of the teaching-learning process in undergraduate courses in Nursing.

PubMed

González-Chordá, Víctor Manuel; Maciá-Soler, María Loreto

2015-01-01

to identify aspects of improvement of the quality of the teaching-learning process through the analysis of tools that evaluated the acquisition of skills by undergraduate students of Nursing. prospective longitudinal study conducted in a population of 60 secondyear Nursing students based on registration data, from which quality indicators that evaluate the acquisition of skills were obtained, with descriptive and inferential analysis. nine items were identified and nine learning activities included in the assessment tools that did not reach the established quality indicators (p<0.05). There are statistically significant differences depending on the hospital and clinical practices unit (p<0.05). the analysis of the evaluation tools used in the article "Nursing Care in Welfare Processes" of the analyzed university undergraduate course enabled the detection of the areas for improvement in the teachinglearning process. The challenge of education in nursing is to reach the best clinical research and educational results, in order to provide improvements to the quality of education and health care.

"Of Course I'm Communicating; I Lecture Every Day": Enhancing Teaching and Learning in Introductory Statistics. Scholarship of Teaching and Learning

ERIC Educational Resources Information Center

Wulff, Shaun S.; Wulff, Donald H.

2004-01-01

This article focuses on one instructor's evolution from formal lecturing to interactive teaching and learning in a statistics course. Student perception data are used to demonstrate the instructor's use of communication to align the content, students, and instructor throughout the course. Results indicate that the students learned, that…

Students' Perception of the Condition of Their Classroom Physical Learning Environment and Its Impact on Their Learning and Motivation

ERIC Educational Resources Information Center

Asiyai, Romina

2014-01-01

This study examined the perception of secondary school students on the condition of their classroom physical learning environment and its impact on their learning and motivation. Four research questions were asked and answered using descriptive statistics while three hypotheses were formulated and tested using t-test statistics at 0.05 level of…

Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities without Awareness

ERIC Educational Resources Information Center

Turk-Browne, Nicholas B.; Scholl, Brian J.; Chun, Marvin M.; Johnson, Marcia K.

2009-01-01

Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during…

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Methods for Assessment of Memory Reactivation.

PubMed

Liu, Shizhao; Grosmark, Andres D; Chen, Zhe

2018-04-13

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Assessment of Problem-Based Learning in the Undergraduate Statistics Course

ERIC Educational Resources Information Center

Karpiak, Christie P.

2011-01-01

Undergraduate psychology majors (N = 51) at a mid-sized private university took a statistics examination on the first day of the research methods course, a course for which a grade of "C" or higher in statistics is a prerequisite. Students who had taken a problem-based learning (PBL) section of the statistics course (n = 15) were compared to those…

Listening through Voices: Infant Statistical Word Segmentation across Multiple Speakers

ERIC Educational Resources Information Center

Graf Estes, Katharine; Lew-Williams, Casey

2015-01-01

To learn from their environments, infants must detect structure behind pervasive variation. This presents substantial and largely untested learning challenges in early language acquisition. The current experiments address whether infants can use statistical learning mechanisms to segment words when the speech signal contains acoustic variation…

Learning Essential Terms and Concepts in Statistics and Accounting

ERIC Educational Resources Information Center

Peters, Pam; Smith, Adam; Middledorp, Jenny; Karpin, Anne; Sin, Samantha; Kilgore, Alan

2014-01-01

This paper describes a terminological approach to the teaching and learning of fundamental concepts in foundation tertiary units in Statistics and Accounting, using an online dictionary-style resource (TermFinder) with customised "termbanks" for each discipline. Designed for independent learning, the termbanks support inquiring students…

Influence of a veterinary curriculum on the approaches and study skills of veterinary medical students.

PubMed

Chigerwe, Munashe; Ilkiw, Jan E; Boudreaux, Karen A

2011-01-01

The objectives of the present study were to evaluate first-, second-, third-, and fourth-year veterinary medical students' approaches to studying and learning as well as the factors within the curriculum that may influence these approaches. A questionnaire consisting of the short version of the Approaches and Study Skills Inventory for Students (ASSIST) was completed by 405 students, and it included questions relating to conceptions about learning, approaches to studying, and preferences for different types of courses and teaching. Descriptive statistics, factor analysis, Cronbach's alpha analysis, and log-linear analysis were performed on the data. Deep, strategic, and surface learning approaches emerged. There were a few differences between our findings and those presented in previous studies in terms of the correlation of the subscale monitoring effectiveness, which showed loading with both the deep and strategic learning approaches. In addition, the subscale alertness to assessment demands showed correlation with the surface learning approach. The perception of high workloads, the use of previous test files as a method for studying, and examinations that are based only on material provided in lecture notes were positively associated with the surface learning approach. Focusing on improving specific teaching and assessment methods that enhance deep learning is anticipated to enhance students' positive learning experience. These teaching methods include instructors who encourage students to be critical thinkers, the integration of course material in other disciplines, courses that encourage thinking and reading about the learning material, and books and articles that challenge students while providing explanations beyond lecture material.

Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences

PubMed Central

2014-01-01

Background While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences. Methods 121 first year graduate entry medical students completed the Survey of Attitudes toward Statistics instrument together with information on demographics and prior learning experiences. Results Students tended to appreciate the relevance of statistics in their professional life and be prepared to put effort into learning statistics. They had neutral to positive attitudes about their interest in statistics and their intellectual knowledge and skills when applied to it. Their feelings towards statistics were slightly less positive e.g. feelings of insecurity, stress, fear and frustration and they tended to view statistics as difficult. Even though 85% of students had taken a quantitative course in the past, only 24% of students described it as likely that they would take any course in statistics if the choice was theirs. How well students felt they had performed in mathematics in the past was a strong predictor of many of the components of attitudes. Conclusion The teaching of statistics to medical students should start with addressing the association between students’ past experiences in mathematics and their attitudes towards statistics and encouraging students to recognise the difference between the two disciplines. Addressing these issues may reduce students’ anxiety and perception of difficulty at the start of their learning experience and encourage students to engage with statistics in their future careers. PMID:24708762

Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences.

PubMed

Hannigan, Ailish; Hegarty, Avril C; McGrath, Deirdre

2014-04-04

While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences. 121 first year graduate entry medical students completed the Survey of Attitudes toward Statistics instrument together with information on demographics and prior learning experiences. Students tended to appreciate the relevance of statistics in their professional life and be prepared to put effort into learning statistics. They had neutral to positive attitudes about their interest in statistics and their intellectual knowledge and skills when applied to it. Their feelings towards statistics were slightly less positive e.g. feelings of insecurity, stress, fear and frustration and they tended to view statistics as difficult. Even though 85% of students had taken a quantitative course in the past, only 24% of students described it as likely that they would take any course in statistics if the choice was theirs. How well students felt they had performed in mathematics in the past was a strong predictor of many of the components of attitudes. The teaching of statistics to medical students should start with addressing the association between students' past experiences in mathematics and their attitudes towards statistics and encouraging students to recognise the difference between the two disciplines. Addressing these issues may reduce students' anxiety and perception of difficulty at the start of their learning experience and encourage students to engage with statistics in their future careers.

Evaluation of Deep Learning Representations of Spatial Storm Data

NASA Astrophysics Data System (ADS)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Bringing Grand Canyon to the College Campus: Assessment of Student Learning in the Geosciences Through Virtual Field Trip Games for Mobile Smart-Devices

NASA Astrophysics Data System (ADS)

Bursztyn, N.; Walker, A.; Shelton, B.; Pederson, J. L.

2015-12-01

Geoscience educators have long considered field trips to be the most effective way of attracting students into the discipline. A solution for bringing student-driven, engaging, kinesthetic field experiences to a broader audience lies in ongoing advances in mobile-communication technology. This NSF-TUES funded project developed three virtual field trip experiences for smartphones and tablets (on geologic time, geologic structures, and hydrologic processes), and then tested their performance in terms of student interest in geoscience as well as gains in learning. The virtual field trips utilize the GPS capabilities of smartphones and tablets, requiring the students to navigate outdoors in the real world while following a map on their smart device. This research, involving 873 students at five different college campuses, used analysis of covariance (ANCOVA) and multiple regression for statistical methods. Gains in learning across all participants are minor, and not statistically different between intervention and control groups. Predictors of gains in content comprehension for all three modules are the students' initial interest in the subject and their base level knowledge. For the Geologic Time and Structures modules, being a STEM major is an important predictor of student success. Most pertinent for this research, for Geologic Time and Hydrologic Processes, gains in student learning can be predicted by having completed those particular virtual field trips. Gender and race had no statistical impact, indicating that the virtual field trip modules have broad reach across student demographics. In related research, these modules have been shown to increase student interest in the geosciences more definitively than the learning gains here. Thus, future work should focus on improving the educational impact of mobile-device field trips, as their eventual incorporation into curricula is inevitable.

Problem-based learning in comparison with lecture-based learning among medical students.

PubMed

Faisal, Rizwan; Bahadur, Sher; Shinwari, Laiyla

2016-06-01

To compare performance of medical students exposed to problem-based learning and lecture-based learning. The descriptive study was conducted at Rehman Medical College, Peshawar, Pakistan from May 20 to September 20, 2014, and comprised 146 students of 3rd year MBBS who were randomised into two equal groups. One group was taught by the traditional lecture based learning, while problem-based learning was conducted for the other group on the same topic. At the end of sessions, the performance of the two groups was evaluated by one-best type of 50 multiple choice questions. Total marks were 100, with each question carrying 2 marks. SPSS 15 was used for statistical analysis. There were 146 students who were divided into two equal groups of 73(50%) each. The mean score in the group exposed to problem-based learning was 3.2 ± 0.8 while those attending lecture-based learning was 2.7±0.8 (p= 0.0001). Problem-based learning was more effective than lecture based learning in the academic performance of medical students.

The Relationship between Teaching Presence and Student Course Outcomes in an Online International Population

ERIC Educational Resources Information Center

Wendt, Jillian; Courduff, Jennifer

2018-01-01

A causal comparative research design was utilized in this study to examine the relationship between international students' perceptions of teacher presence in the online learning environment and students' achievement as measured by end of course grades. Spearman's analysis indicated no statistically significant correlation between the composite…